Enhancing patient referral outcome through structuring data for efficient place of service utilization rate analysis

ABSTRACT

Disclosed are a method, a device, and/or a system of enhancing patient referral outcome through structuring data for efficient place of service utilization rate analysis. In one embodiment, a device extracts a set of utilization logs that each describe a type of facility from a log data structure modeling healthcare providers associated by referral logs. A referral request agent receives a referral profile generated for a patient. The referral profile is compared by a profile matching engine to the set of utilization logs. A dataset reduction subroutine generates a reduced dataset that is compared to a minimum threshold of healthcare providers to determine a sufficient number are within the reduced dataset. The utilization rate routine then calculates a POS utilization rate, and a healthcare provider is selected based on criteria including the POS utilization rate. The selection can be transmitted to a computing device of a healthcare provider.

CLAIM FOR PRIORITY

This patent application claims priority from, and hereby incorporates byreference: U.S. patent application Ser. No. 16/801,710, titled‘MAXIMIZING PATIENT REFERRAL OUTCOME THROUGH HEALTHCARE UTILIZATIONAND/OR REFERRAL EVALUATION’, filed Feb. 26, 2020.

FIELD OF TECHNOLOGY

This disclosure relates generally to data processing devices and, moreparticularly, to a method, a device, and/or a system of enhancingpatient referral outcome through structuring data for efficient place ofservice utilization rate analysis.

BACKGROUND

It may be a fundamental interest of a healthcare provider and/or ahealthcare network that a patient has a positive outcome in receivinghealthcare services. An important factor in creating a positive outcomeis ensuring patient accessibility (which may also be referred to as“access”) to the healthcare services. For example, high degree and/orsufficient amount of access may mean that the patient has a healthcareprovider nearby where the patient resides, that the patient is seen bythe healthcare provider promptly, that any subsequent procedures areperformed on time, and/or that special needs of the patient can beaccommodated. What may be another important factor in a positive outcomeis value of the healthcare services provided. Value may be based on anumber of factors, but may for example be based on an appropriatebalancing of monetary cost, healthcare service complexity (e.g.,complexity of a procedure, recovery time, side effects), and/or riskaccess considerations.

The healthcare industry may be facing increasing pressure to providepositive patient outcomes, including increased access and value inproviding healthcare services. For example, payers (such as employers,insurance companies, and government agencies such as the Center forMedicare and Medicaid Services) may be shifting some reimbursementpractice to promote value-based competition.

What may be a major factor in generating positive patient outcomes is anefficient referral process of the patient between healthcare providers.A first healthcare provider, called the “referring provider,” willgenerally refer a patient to a different healthcare provider, called a“referral provider,” when the referring provider is not qualified orotherwise able to provide a needed healthcare service for the patient.Referrals within the healthcare industry are common, especially within ahealthcare network (e.g., an HMO, an ACO). A healthcare provider may bean individual, such as a solo clinician licensed to practice medicine orprovide medical services (who may be referred to as an “individualprovider”). The healthcare provider may also be a hospital, group ofindividual health professionals, or other business entity or otherorganization, some or all of which are licensed to practice a healthprovision or to perform health care services (which may be referred toas a “group provider”). Healthcare providers also generally desire forreferred patients to be transferred efficiently, with an emphasis ongood access to medical services once the patient is transferred to thereferral provider.

The referral process in which the patient is referred from onehealthcare provider to another can pose challenges to maximizing patientoutcome, including access to and/or value of healthcare services. First,the referring provider may generally have little or no ability to gatheror assess information about a referral provider. For example, referralsmay be made based on habit or subjective criteria such as generalnotoriety within a healthcare network rather than awareness of anyinformation related to patient outcome. This may partially occur forpractical reasons because the referring provider has little time (e.g.,20-minute appointments of a clinician) and/or any evaluation assessmentof referral providers may be a discrete process from providing thehealthcare service.

Even where referrals may be optimal at one time (e.g., in terms of valueand/or access), they may later become sub-optimal. Access and/or valueof a referral provider may change on a monthly, daily, or even real-timebasis. For example, even a healthcare provider providing historicallyhigh-value healthcare services may be a poor referral provider at agiven time because they have low access. Such a referral provider mayhave a patient backlog, diminished capacity due to retiring clinicians,or have changed facilities and/or locations.

Access and/or value may also be context dependent, for example varyingbased on the circumstances and/or needs of the patients. A healthcareprovider with generally good access and value for orthopedic surgeriesmay not provide more specific surgery (e.g., hand surgery) with goodaccess or at high value. Conversely, a healthcare provider may be knownto provide excellent access and good value for a given procedure, butmay not excel in a broader category of healthcare services or may notexcel in other defined incentives that may shift behaviors towardincreased value and/or access.

Finally, even where some data-based and/or objective insight may bepresent and available to a clinician at the time a referral must bemade, it may be difficult for a healthcare provider and/or healthcarenetwork to promote its values or policies related to access and/orvalue. For example, there may be a challenge in communicating insightsto the healthcare providers of the network.

One or more of these challenges may result in detriment to one or moreof the stakeholders of a healthcare network. Patients may not receivehealthcare services promptly, may not receive healthcare services bestsuited for their particular needs, and/or may decide not to receivehealthcare services where the value and/or access is low, raising theirhealth risk and potentially long-term cost when a health issue is notefficiently addressed. Healthcare providers working hard to establishgood access and high value may not be rewarded with referrals that wouldreinforce their positive efforts. Healthcare providers lose revenue andmay be at increased risk of malpractice where patients are not met withsufficient access. Payers may pay increased amounts and may thereforecontinue to support sub-optimal referral and/or healthcare utilizationpractices. On what may be a larger scale, a healthcare network maycontinue to operate with inefficiencies, resulting in network-widesub-optimal value and access that can affect patient loyalty, damagereputation and brand stature, increase long-term health care costs,and/or reduce long-term revenue. There is a continuing need fortechnologies that support and increase the efficiency of the referralprocess.

SUMMARY

Disclosed are a method, a device, and/or a system of enhancing patientreferral outcome through structuring data for efficient place of serviceutilization rate analysis. In one embodiment, a device for structuringand processing data for efficient selection of a referral provider for apatient includes a processor, a memory, computer readable instructionsthat when executed extract a set of utilization logs, a referral requestagent, a profile matching engine, a dataset reduction subroutine, autilization rate routine, and a set of computer readable instructionsthat when executed select the first healthcare provider for inclusion ina referral data.

The log data structure includes a set of data each modeling healthcareproviders and each associated by one or more referral logs, and alsoincludes a group of utilization logs each associated with a data fromthe set of data each modeling the healthcare providers. The group ofutilization logs includes a first utilization log of a different patientthat was previously served by a first healthcare provider at a facilityto result in a healthcare utilization log. The first utilization logincludes a provider UID of the first healthcare provider and a place ofservice value that describes a type of facility associated with ahealthcare utilization record.

The referral request agent includes computer readable instructions thatwhen executed generate and/or receive a referral profile generated forthe patient. The profile matching engine includes computer readableinstructions that when executed compare the referral profile to the setof utilization logs. The dataset reduction subroutine includes computerreadable instructions that when executed generate a reduced dataset thatincludes a subset of utilization logs extracted from the set ofutilization logs matching the referral profile. The number of healthcareproviders associated with the reduced dataset is compared to a minimumthreshold of healthcare providers to determine a sufficient number ofhealthcare providers within the reduced dataset.

The utilization rate routine includes computer readable instructionsthat when executed calculates, using the subset of utilization logs inthe reduced dataset, a POS utilization rate of the first healthcareprovider for each instance of the place of service value. The POSutilization rate is a set of percentage values, each percentage valuebegin a number of utilization logs in the reduced dataset that includesan instance of the place of service value within a place of servicerange relative to a total number of utilization logs in the reduceddataset.

The set of computer readable instructions that when executed select thefirst healthcare provider for inclusion in a referral data makes theselection based on criteria that includes the POS utilization rate fortransmission of a name of the first healthcare provider and/or theprovider UID of the first healthcare provider to a computing device forgeneration of a referral selection for the patient.

The device may also include a utilization extraction routine includingcomputer readable instructions that when executed determine generationof the healthcare utilization record and determine the place of servicevalue that describes the type of facility associated with the healthcareutilization record. The healthcare utilization record may include apatient UID of the different patient and the provider UID of the firsthealthcare provider.

The device may also include a log storage module that includes computerreadable instructions that when executed generates the first utilizationlog including the provider UID of the first healthcare provider, theplace of service value, and/or a utilization time associated withproviding a healthcare service to the different patient and/orgeneration of the healthcare utilization record. The log storage modulemay include computer readable instructions that when executed stores autilization log in the log data structure.

The device may include a referral profile generation routine thatincludes computer readable instructions that when executed generates thereferral profile for the patient. The referral profile may include theplace of service range and a time range. A referral request may beinitiated on a user interface of a clinical documentation workflow of apoint-of-care application. The point-of-care application may be run by asecond healthcare provider to refer the patient of the second healthcareprovider to the referral provider.

A second set of computer readable instructions may also be included inthe device, that when executed, apply a utilization ruleset to score,rank, and/or qualify the first healthcare provider selected based oncriteria including the POS utilization rate and add the provider UID ofthe first healthcare provider to the referral data.

The device can also include a third set of computer readableinstructions that when executed transmit the referral data over anetwork to the computing device. The computing device may be utilized bya second healthcare provider and may be running a point-of-careapplication. The referral data may be integrated within a user interfaceof a clinical documentation workflow of the point-of-care application.

The device may include a patient query engine that includes computerreadable instructions that when executed query a patient profile of thedifferent patient with a patient UID of the different patient andextract from the patient profile of the different patient a patient dataincluding a demographic data of the different patient, a coverage typeof the different patient, and/or a diagnosis code of the differentpatient. The first utilization log may further include the patient data.The referral profile may further include a patient data range. Thepatient data range may include a demographic data of the patient, acoverage type of the patient, and/or a diagnosis code of the patient.

The utilization rate routine may also include computer readableinstructions that when executed calculates, using a different set ofutilization logs of two or more healthcare providers, a POS utilizationrate of the two or more healthcare providers for each instance of theplace of service value within the place of service range. The selectionof the first healthcare provider may be based on criteria including thePOS utilization rate of the first healthcare provider relative to astatistical average of the POS utilization rate of the two or morehealthcare providers.

The device may further include a referral rate routine that includescomputer readable instructions that when executed calculates, using thesubset of the set of referral logs in the reduced dataset, an inboundre-referral rate of the referral healthcare provider. The inboundre-referral rate can be calculated as a proportion of (i) the subset ofthe set of referral logs that each store a database association drawninto the data modeling the referral healthcare provider and thatcomprise a database association linked to one or more of the subset ofthe set of referral logs that store a database association drawn out ofthe data modeling the referral healthcare provider (where a timestamp ofeach referral log storing the database association drawn into the datamodeling the referral healthcare provider and a timestamp of eachreferral log storing the database association drawn out of the datamodeling the referral healthcare provider are within a first time periodvalue), relative to (ii) other instances within the subset of the set ofreferral logs that each store database associations drawn into the datamodeling the referral healthcare provider. The place of service valuemay be stored in computer memory as a POS code value, and the place ofservice range, a patient data range, and/or a time range of the referralprofile is selected by the clinician through the point-of-careapplication.

In another embodiment, a method for structuring and processing data forefficient selection of a referral provider for a patient includesextracting a set of utilization logs from a log data structure. The logdata structure includes a set of data each modeling healthcare providersand each associated by one or more referral logs, and also includes agroup of utilization logs each associated with a data from the set ofdata each modeling the healthcare providers. The group of utilizationlogs includes a first utilization log of a different patient that waspreviously served by a first healthcare provider at a facility to resultin a healthcare utilization log. The first utilization log includes aprovider UID of the first healthcare provider and a place of servicevalue that describes a type of facility associated with a healthcareutilization record.

The method receives a referral profile generated for the patient,compares the referral profile to the set of utilization logs, andgenerates a reduced dataset that includes a subset of utilization logsextracted from the set of utilization logs matching the referralprofile. The number of healthcare providers associated with the reduceddataset is compared to a minimum threshold of healthcare providers todetermine a sufficient number of healthcare providers within the reduceddataset. The method calculates, using the subset of utilization logs inthe reduced dataset, a POS utilization rate of the first healthcareprovider for each instance of the place of service value. The POSutilization rate is a set of percentage values, each percentage value anumber of utilization logs in the reduced dataset that include aninstance of the place of service value within a place of service rangerelative to a total number of utilization logs in the reduced dataset.The method then selects the first healthcare provider for inclusion in areferral data based on criteria that includes the POS utilization rate.The referral data is usable for transmission of a name of the firsthealthcare provider and/or the provider UID of the first healthcareprovider to a computing device for generation of a referral selectionfor the patient.

The method may determine generation of the healthcare utilizationrecord. The healthcare utilization record includes a patient UID of thedifferent patient and the provider UID of the first healthcare provider.The place of service value that describes the type of facilityassociated with the healthcare utilization record is determined. Themethod generates the first utilization log that includes the providerUID of the first healthcare provider, the place of service value, and autilization time associated with providing a healthcare service to thedifferent patient and/or generation of the healthcare utilizationrecord. The utilization log may be stored in the log data structure.

The referral profile for the patient may be generated, the referralprofile including the place of service range and a time range. Thereferral request may be initiated on a user interface of a clinicaldocumentation workflow of a point-of-care application that is run by asecond healthcare provider to refer the patient of the second healthcareprovider to the referral provider.

The method includes applying a utilization ruleset to score, rank,and/or qualify the first healthcare provider selected based on criteriathat may include the POS utilization rate. The provider UID of the firsthealthcare provider may be added to the referral data. The referral datacan be transmitted over a network from a server to the computing device.The computing device may be utilized by a second healthcare provider andis running a point-of-care application, and the referral data may beintegrated within a user interface of a clinical documentation workflowof the point-of-care application.

The method may also query a patient profile of the different patientwith a patient UID of the different patient and extract from the patientprofile of the different patient a patient data that may include ademographic data of the different patient, a coverage type of thedifferent patient, and/or a diagnosis code of the different patient. Thefirst utilization log further may include the patient data and thereferral profile may further include a patient data range. The patientdata range may include a demographic data of the patient, a coveragetype of the patient, and/or a diagnosis code of the patient.

The method may also calculate, using a different set of utilization logsof two or more healthcare providers, a POS utilization rate of the twoor more healthcare providers for each instance of the place of servicevalue within the place of service range. The selection of the firsthealthcare provider is based on criteria comprising the POS utilizationrate of the first healthcare provider relative to a statistical averageof the POS utilization rate of the two or more healthcare providers.

A selection of a second healthcare provider may be received from aclinician through a point-of-care application and an appointment for thepatient automatically scheduled with the first healthcare provider. Themethod may also extract from a patient profile of the patient a locationdata associated with the patient, determine the second healthcareprovider is within a predetermined distance based on the location data,and determine a type of service associated with the healthcareutilization record. The type of service may be specified by a type ofservice value that may be a service category and/or a procedure codevalue. The procedure code value may be a CPT code value. The utilizationruleset may determine that the instance of the place of service valuemay be a preferred instance of the place of service value based oncriteria comprising the type of service. The place of service value maybe stored in computer memory as a POS code value, and the place ofservice range, a patient data range, and/or a time range of the referralprofile may be selected by the clinician through the point-of-careapplication.

In yet another embodiment, a computer readable media includes computerexecutable instructions that when executed extract a set of utilizationlogs from a log data structure. The log data structure includes (i) aset of data each modeling healthcare providers and each associated byone or more referral logs, and (ii) a group of utilization logs eachassociated with a data from the set of data each modeling the healthcareproviders. The group of utilization logs includes a first utilizationlog of a different patient that was previously served by a firsthealthcare provider at a facility to result in a healthcare utilizationlog. The first utilization log includes a provider UID of the firsthealthcare provider and a place of service value that describes a typeof facility associated with a healthcare utilization record.

The computer readable media includes computer executable instructionsthat when executed receive a referral profile generated for the patient,compare the referral profile to the set of utilization logs, andgenerate a reduced dataset including a subset of utilization logsextracted from the set of utilization logs matching the referralprofile. The number of healthcare providers associated with the reduceddataset is compared to a minimum threshold of healthcare providers todetermine a sufficient number of healthcare providers within the reduceddataset.

Using the subset of utilization logs in the reduced dataset, computerexecutable instructions included on the computer readable media, whenexecuted, calculates a POS utilization rate of the first healthcareprovider for each instance of the place of service value. The POSutilization rate is a set of percentage values, each percentage value anumber of utilization logs in the reduced dataset that includes aninstance of the place of service value within a place of service rangerelative to a total number of utilization logs in the reduced dataset.

The computer readable media includes computer executable instructionsthat when executed select the first healthcare provider for inclusion ina referral data based on criteria that includes the POS utilization ratefor transmission of a name of the first healthcare provider and/or theprovider UID of the first healthcare provider to a computing device forgeneration of a referral selection for the patient.

Other features of the present embodiments will be apparent from theaccompanying drawings and from the detailed description that follows.

BRIEF DESCRIPTION OF THE DRAWINGS

The embodiments of this disclosure are illustrated by way of example andnot limitation in the figures of the accompanying drawings, in whichlike references indicate similar elements and in which:

FIG. 1 illustrates a referral evaluation network comprising one or morehealthcare providers generating referral records and/or utilizationrecords stored on one or more record servers, a log server extractingand structuring utilization logs (which may also referred to ashealthcare utilization logs) and/or referral logs (which may also bereferred to as healthcare referral logs) from the utilization recordsand/or referral records, a computing device of a healthcare provider(acting as a referring provider) to generate a referral request and areferral profile, a referral server generating evaluation data based ona match of the referral profile to a set of the utilization logs and/orreferral logs, resulting in generation of actionable referralinformation for a clinician and/or the patient during a referralprocess, and a network, according to one or more embodiments.

FIG. 2 illustrates the log server of FIG. 1 , including a referraldatabase storing one or more referral logs, a utilization databasestoring one or more utilization logs, a place-of-service (POS)extraction sub-routine for determining a POS value of a location and/ora facility utilized by a healthcare provider in providing a healthcareservice, a type of service (TOS) extraction subroutine for determining atype of service value associated with the healthcare service, and a logassociation system for determining associations between referral logsand/or utilization logs, according to one or more embodiments.

FIG. 3 illustrates the referral server of FIG. 1 , including a patientreferral profile matching algorithm for comparing the referral profileto one or more utilization logs and/or one or more referral logs, adataset reduction subroutine, a utilization rate routine for assessingplace of service and type of service of a healthcare provider, areferral rate routine for determining detrimental and/or positivereferral activity of a healthcare provider, a utilization ruleset, areferral ruleset, and a referral data including one or more healthcareproviders determined to score highly, rank highly, and/or or qualify asa referral provider, according to one or more embodiments.

FIG. 4 illustrates a computing device utilized by the healthcareprovider who is the referring provider of FIG. 1 , including apoint-of-care application and clinical documentation workflow for directinteraction between a patient and a clinician, a referral profilegeneration routine for matching against one or more of the logs of thelog server of FIG. 3 , and an integration routine for receiving anddisplaying the referral data, according to one or more embodiments.

FIG. 5 illustrates one or more records servers housing a patient profiledatabase storing a profile of one or more patients, a provider databasestoring a profile of one or more healthcare providers, an EMR databasehaving the electronic medical records of one or more patients, and aclaims database storing one or more healthcare claims, according to oneor more embodiments.

FIG. 6 is a utilization log generation process flow illustratingextraction of utilization data from a utilization record, determinationof a POS and TOS associated with the utilization record, augmentationthrough a patient data (e.g., that may include a demographic data and aninsurance coverage type), and storage as a utilization log, according toone or more embodiments.

FIG. 7 is a first referral profile generation process flow illustratinginitiation and generation of a referral profile, according to one ormore embodiments.

FIG. 8 is a first referral evaluation process flow illustrating matchingof the referral profile to one or more utilization logs, generation of areduced dataset, calculation of one or more POS utilization rates forone or more healthcare providers, application of a utilization rulesetto score, rank, and/or qualify one or more healthcare providers, andgeneration of a referral data comprising one or more healthcareproviders, according to one or more embodiments.

FIG. 9 is a referral selection process flow illustrating receivingreferral data on a computing device, integrating the referral datawithin a clinical documentation workflow, optionally limiting the one ormore healthcare providers by geographical area, and receiving aselection of a referral provider, according to one or more embodiments.

FIG. 10 illustrates a first instance of a log data structure that may beutilized in storing the referral logs and/or the utilization logs ofFIG. 2 , the log data structure demonstrating database associationsdefined between related instances of the referral logs and/or theutilization logs, according to one or more embodiments.

FIG. 11 is a referral log process flow illustrating detection of ahealthcare referral of a patient, determination of the TOS valueassociated with the healthcare referral, storage of a first referrallog, determination that the first referral log corresponds to a secondreferral log, and association between the first referral log and thesecond referral log (e.g., through the database association of FIG. 10), according to one or more embodiments.

FIG. 12 is a utilization log association process flow illustratingdetermination of an association between a referral log and a utilizationlog (e.g., through a database association of FIG. 10 ), according to oneor more embodiments.

FIG. 13 is a second referral profile generation process flowillustrating initiation and generation of the referral profile,according to one or more embodiments.

FIG. 14 is a second referral evaluation process flow illustratingmatching of the referral profile to one or more referral logs,generation of a reduced dataset, calculation of a re-referral rate,service rate, and/or service time for one or more healthcare providers,and application of a referral ruleset to score, rank, and/or qualify oneor more healthcare providers, according to one or more embodiments.

FIG. 15 is a patient selection process flow illustrating selection of areferral provider directly by a patient through a computing device ofthe patient such as a mobile device (e.g., smartphone), according to oneor more embodiments.

FIG. 16 illustrates a second log data structure in which referral logsand/or utilization logs are associated with both group providers andindividual providers within each group provider, according to one ormore embodiments.

FIG. 17 is a provider evaluation process flow illustrating analysis ofthe logs of one or more group providers and one or more individualproviders to generate a referral data based on a weighted average ofre-referral rates, according to one or more embodiments.

FIG. 18 illustrates an example of generation of a referral data based onthe patient profile matched to logs of the reduced dataset, according toone or more embodiments.

Other features of the present embodiments will be apparent from theaccompanying drawings and from the detailed description that follows.

DETAILED DESCRIPTION

Disclosed are a method, a device, and a system of enhancing patientreferral outcome through structuring data for efficient place of serviceutilization rate analysis. Although the present embodiments have beendescribed with reference to specific example embodiments, it will beevident that various modifications and changes may be made to theseembodiments without departing from the broader spirit and scope of thevarious embodiments.

FIG. 1 illustrates a referral evaluation network, according to one ormore embodiments. Instances of a healthcare provider 102, shown as thehealthcare provider 102A through the healthcare provider 102N, mayprovide healthcare services to instances of the patient 105. In one ormore embodiments, one or more of the healthcare providers 102 may bewithin a healthcare network, for example an HMO or ACO. In the course ofproviding a healthcare service to a patient 105, a healthcare provider102 may generate a utilization record 502, for example an insuranceclaim that can be analyzed for information about the healthcare servicesprovided to the patient 105 (e.g., the healthcare claim record 541 ofFIG. 5 ). In the course of making a referral for the patient 105, afirst instance of a healthcare provider 102, referred to as a “referringprovider,” may generate a referral record 504. The referral record 504,for example, may be a referral report generated when a referral is madethrough scheduling software of the healthcare network. Referral records504 and/or utilization records 502 are communicated over a network 101and stored on one or more instances of a record server 500, shown as therecord servers 500A through 500N. The record server 500A, for example,may include a claims database 540, as further shown and described in theembodiment of FIG. 5 . An instance of the record server 500 may alsostore additional data, for example a patient EMR database 520, storingdata usable in generating logs that can be evaluated to determine valueof healthcare services and/or patient 105 access to a healthcareprovider 102. The network 101 may be a local area network (LAN), avirtual private network (VPN), a wide area network (WAN), and/or theInternet.

A log server 200 extracts data from records, generates logs (e.g., autilization log 271 and a referral log 231), and may associate the logswhere such associations are identified that associates the logs. Autilization extraction routine 204 extracts data from the utilizationrecord 502, which the log server 200 stores in a utilization log 271within a utilization database 270. Similarly, the log server 200 detectsa referral record 504, uses a referral extraction routine 206 to extractdata from the referral record 504, and structures and stores a referrallog 231 in a referral database 230. The utilization log 271 may also bereferred to as the healthcare utilization log 271. The utilization log271, for example, may store data such as a provider identifier (e.g.,the provider UID 532) of a healthcare provider 102 that provided thehealthcare services to a patient 105, a place of service value 250designating a location and/or facility at which the healthcare servicewas provided, a type of service value 252, a set of patient data (e.g.,the patient data 514 of FIG. 5 ), and/or a set of relation data definingone or more database associations between the utilization log 271 andother logs as shown and described in the embodiments of FIG. 2 , FIG. 10, and FIG. 16 . The referral log 231, for example, may store data suchas a provider identifier of a referring provider, a provider identifierof a referral provider, a type of service value 252, a referral time254, the patient data 514, and the relation data 260 defining one ormore database associations between the referral log 231 and one or moreother logs. In one or more embodiments, a set of associated logs maystore data related to, modeling, and/or defining an episode of care ofthe patient 105.

The log server 200 further comprises a log association system 216 whichmay identify associations between one or more logs, for example betweena referral log 231 and a utilization log 271, between a first referrallog 231A and a second referral log 231B, and/or between a firstutilization log 271A and a second utilization log 271B. Examples of thereferral log 231 and the utilization log 271 are shown and described indetail in the embodiment of FIG. 2 , and examples of data structuresdefining associations between two or more logs are shown and describedin conjunction with FIG. 10 and FIG. 16 .

In the embodiment of FIG. 1 , a healthcare provider 102A acting as areferring provider initiates a referral request to refer the patient 105to an unspecified instance of the healthcare provider 102 as a referralprovider. In one or more embodiments, the healthcare provider 102A maygenerate the referral in a clinical setting, for example at anappointment where a clinician 106 of the healthcare provider 102A isproviding consultation and/or treatment to the patient 105. Theclinician 106 may initiate the referral request within a point-of-careapplication 404 (also referred to herein as the POC application 404)running on the computing device 400A. The computing device 400A, forexample, may be a desktop computer, a tablet device, a mobile device, oranother computer suitable for running the point-of-care application.

The computing device 400A may generate a referral profile 430. Thereferral profile 430, for example, may include a place of service range450, a type of service range 452, and a patient data range 456, as shownand described in FIG. 4 . The referral profile 430 may be automaticallyand/or manually generated, for example by asking the clinician 106questions and/or providing a user interface (UI) workflow for theclinician 106 to assign a type of service range 452, by automaticallyretrieving patient data from an EMR of the patient 105 (e.g., adiagnosis code), and/or by accommodating manual input while permittingthe clinician 106 to ask questions of the patient 105. The referralprofile 430 is communicated through the network 101 to the referralserver 300.

The referral server 300 receives the referral profile 430 and inconjunction with the utilization logs 271 and/or the referral logs 231evaluates probable value and/or access of one or more of the instancesof the healthcare provider 102B through the healthcare provider 102N toreturn actionable information and identify potential referral providersfor selection by the healthcare provider 102A. Access and/or value maybe based on a variety of rates and evaluations described in one or moreof the present embodiments. After receiving the referral profile 430over the network 101, a matching engine 308 may compare the referralprofile 430 to a log dataset 290 of one or more utilization logs 271and/or more referral logs 231. The profile matching engine 308determines matches within the ranges specified, for example instances ofthe utilization log 271 having a type of service value 252 within thetype of service range 452.

In one or more embodiments, the value and/or access evaluation may bedetermined with respect to the type of service range 452 and the placeof service range 450 specified in the referral profile 430, such thatlogs within the type of service range 452 and/or the place of servicerange 450 are utilized to generate a reduced dataset (e.g., the reduceddataset 390 of FIG. 3 ) which may increase evaluation relevance for aparticular instance of the patient 105.

A utilization rate routine 312 can generate utilization rates for eachhealthcare provider 102 within the reduced dataset that are indicativeof value and/or access. However, in one or more embodiments, theutilization rate routine 312 may calculate a rate of usage of each placeof service value 250 in providing healthcare services within the type ofservice range 452 of the patient 105, which may be referred to as POSutilization rate 332 as shown and described in FIG. 3 . The utilizationrate routine is further shown and described in conjunction with theembodiment of FIG. 3 . The output of the utilization rate routine 312may include the referral evaluation data 330. A utilization ruleset 316may then be applied to score, rank, qualify, and/or disqualify one ormore healthcare providers 102 within the reduced dataset (e.g., thereduced dataset 390 of FIG. 3 ) to generate the referral data 340comprising data identifying one or more healthcare providers 102 andoptionally additional information such as the referral evaluation data330.

In addition, or in the alternative, the referral server 300 may alsoexecute the referral rate routine 314 which can generate referral ratesfor each healthcare provider 102 within the reduced dataset 390 wheresuch referral rates are indicative of value and/or access. In one ormore embodiments, the referral rate routine 314 may calculate a rate atwhich a referral provider (an instance of the healthcare provider 102who receives a referral) had a referring provider (an instance of thehealthcare provider 102 who issues the referral) re-refer the patient105 within a given timeframe and within the type of service range 452 ofthe patient 105. Such a re-referral event may be referred to as theinbound re-referral rate 334 as shown and described in FIG. 3 . Ratherthan relying on marketing statements, scheduling software, and/oroutdated information, the inbound re-referral rate 334, including in oneor more embodiments as may be narrowed by the referral profile 430, maybe a strong indicator of good access. The output of the referral rateroutine 314 may also be the referral evaluation data 330. The referralruleset 318 may then score, rank, qualify, and/or disqualify one or morehealthcare providers 102 based on the referral evaluation data 330. Ahybrid ruleset 319, shown in the embodiment of FIG. 3 , may also beapplied to both utilization rates and referral rates within the referralevaluation data 330. The utilization ruleset 316, the referral ruleset318, and/or the hybrid ruleset 319 may also be used to implementpriorities of a healthcare network or other organization running the logserver 200 and/or the referral server 300. For example, a healthcareprovider 102 with low value across many categories and/or types ofhealthcare services may have its score reduced even where the scorewould otherwise be higher for the selected referral profile 430 (e.g.,to incentivize general performance increase).

The outcome of the utilization ruleset 316, the referral ruleset 318,and/or the hybrid ruleset 319 is the referral data 340 specifying one ormore instances of the healthcare provider 102, and optionally theirassociated scores, ranks, qualification, and/or underlying referralevaluation data 330 or associated visualizations.

The referral data 340 is returned to the healthcare provider 102A overthe network 101 to the computing device 400A. In one or moreembodiments, the referral data is integrated into the POC application404, and/or a user interface of a clinical documentation workflow. Thisdelivery, integration, and display may occur without the clinician 106ever exiting a software workflow after initiating the referral request.From a user experience of the clinician 106, for example, the clinician106 may have (i) initiated the referral request, (ii) generated andsubmitted a referral profile 430, and then (iii) received a list of oneor more probabilistically high value and/or good access healthcareproviders to choose from to refer the patient 105. The healthcareprovider 102A, for example through the clinician 106, may select thereferral provider from the list of one or more healthcare providers 102in the referral data 340 (e.g., the healthcare provider 102B). Theselection may then be returned to the referral server 300. A referralgeneration sub-routine may automatically initiate the referral of thepatient 105 from the healthcare provider 102A as the referring providerto the healthcare provider 102B as the referral provider. The initiationmay include a manual and/or automatic scheduling process.

In one or more alternate embodiments, the computing device 400A may bereplaced with a computing device of the patient 105, where the patient105 may initiate the referral request and/or select a referral providerfrom the referral data 340. A referral initiated by the patient 105 anda selection of the healthcare provider 102 by the patient 105 is shownand described in conjunction with the embodiment of FIG. 15 .

The healthcare provider 102 may be an individual provider 103 or a groupprovider 104 comprising one or more instances of the individual provider103. Each healthcare provider 102 includes one or more instances of aclinician 106 licensed to practice medicine and/or provider healthcareservices. A clinician may be an individual provider 103. The healthcareservice may be, for example, treatment, diagnosis, health evaluations,medical procedures, laboratory testing, medical imaging, diagnostictesting, and/or surgical procedures.

A utilization record 502 may also include a referral report sent to thereferring provider by the referral provider following the performance ofthe referred service, containing such information as the date ofservice, the type of service performed, and the result of the service. Areferral record 504 may also include an order (digital or an electronicscan of paper), a paper record generated at the point of care of thereferring provider, and/or data generated during a referral providerintake process. The POC application 404 may be a commercially availablepoint of care software such as AthenaNet or eClinicalWorks.

FIG. 2 illustrates a log server 200, according to one or moreembodiments. The log server includes a processer 201 that is a computerprocessor and a memory 203 that is a computer memory, and may furtherinclude computer storage (not shown in the embodiment of FIG. 2 ). Thememory 203 and/or the storage may store computer readable instructionsof various routines, subroutines, engines, modules, and systems, and mayalso store the referral database 230 and/or the utilization database270.

A utilization determination routine 202 comprises computer readableinstructions that when executed on a processor determines generation ofa utilization record 502 of healthcare services. The utilization record502 may comprise a patient UID of a patient 105 and a provider UID of ahealthcare provider 102 (e.g., associated with providing the healthcareservices that the patient 105 utilized). For example, the utilizationdetermination routine 202 may be alerted to a new entry in the EMR 521of the patient 105, may identify a new record in the claim database 540,or may identify a new referral report received by the referring providerfrom the referral provider through an electronic link between theelectronic health record system (e.g., utilizing a first instance of theEMR database 520A) of the referral provider and the electronic healthrecord system of the referring provider (e.g., utilizing a secondinstance of the EMR database 520B).

A utilization extraction routine 204 comprises computer readableinstructions that when executed on a processor extracts the provideridentifier (e.g., a provider UID 532) from the utilization record 502and initiates collection of data to be transformed into a log. Theutilization extraction routine 204 also optionally extracts a set ofpatient data 256, if available in the utilization record 502, and/or apatient identifier (e.g., a patient UID 512) which may be used todetermine additional aspects of the patient data 256. For example, adiagnosis code 524 may be extractable directly from the utilizationrecord 502, whereas the demographic data 515 may need to be queried andreturned from the EMR 521 and/or patient profile 511 using the patientUID 512.

A referral detection routine 205 comprises computer readableinstructions that when executed on a processor detects a healthcarereferral of a patient 105, the healthcare referral from a firsthealthcare provider 102A (e.g., the referring provider) to a secondhealthcare provider 102B (e.g., the referral provider). For example, ahealthcare referral may be detected by receiving a referral report. Inanother example, a healthcare referral may be detected when a softwarecode of an EMR management software generates a referral. The detectionmay even occur manually. For example, following generation of autilization record 502, the referral detection routine 205 may query adevice of the patient 105 and/or the clinician 106 (e.g., through anemail, a push notification, a text message) to ask whether the patient105 was referred, and if so by which healthcare provider 102 acting as areferring provider.

A referral extraction routine 206 comprises computer readableinstructions that when executed on a processor extracts the provider UID532 of the referring provider, the provider UID 532 of the referralprovider, and initiates collection of data to be transformed into a log.The utilization extraction routine 204 also optionally extracts a set ofpatient data 256, if available in the referral record 504, and/or apatient UID 512 which may be used to determine additional aspects of thepatient data 256. For example, a diagnosis code 524 may be extractabledirectly from the utilization record 502, whereas the demographic data515 may need to be queried from the EMR 521 and/or patient profile 511using the patient UID.

A POS extraction subroutine 208 comprises computer readable instructionsthat when executed on a processor determines the place of service value250 (which may also be referred to as the POS value 250) associated withthe utilization record 502. In one or more embodiments, the POS value250 may be a place of service code value maintained by the Centers forMedicare & Medicaid Services (CMS) of the United States Department ofHealth and Human Services, which may be two-digit codes placed on healthcare claims to indicate the setting in which a service was provided.Place of service code values maintained by CMS may be referred to hereinas the CMS POS codes. The CMS POS code may be extracted from ahealthcare claim 541. In one or more other embodiments, the place ofservice value 250 may be inferred, for example where a provider profile531 is associated with a single location and/or facility where theprovider could have performed the healthcare service and/or wherecertain insurance reimbursement codes may implicate a certain place ofservice (e.g., an emergency service, an imaging system only available ata certain facility, etc.). In another example, a place of service codemay be inferred because the type of service is capable of beingperformed in one place of service only (for example, an “inpatienthospital stay” service is definitionally performed in a hospital). Inyet another example, a place of service may be inferred from a facilityclaim (e.g., which may include a fee for utilizing the facility) thatmay be associated with a professional claim (e.g., which may include afee for having received services, treatment, or other health care form aprofessional). In such case, the professional service may be determinedto have been rendered at the place of service. The association may bedetermined, for example, by comparing a name of the patient 105 and adate on which the service was rendered in both the professional fee andthe facility fee.

A TOS extraction subroutine 210 comprises computer readable instructionsthat when executed on a processor determines that one or more instancesof a type of service value 252 associated with the utilization record502 and may extract and store the one or more instances of the type ofservice value 252. For example, the TOS extraction subroutine 210 mayextract a procedure code 548 from a healthcare claim 541. The procedurecode 548 may be a code from the Healthcare Common Procedure CodingSystem (HCPCS) and/or the American Medical Association's CurrentProcedural Terminology (CPT) and/or the International StatisticalClassification of Disease and Related Health Problems (ICD). The TOSextraction subroutine 210 may also employ natural language processingsoftware and/or artificial neural network language processing todetermine words associated with healthcare services and match the wordsagainst a database of known procedures and services, not shown in theembodiment of FIG. 2 . The type of service value 252 may also beidentified by a commonly used and/or bespoke grouping of ICD, CPT, orHCPCS codes (for example, a commercial “code grouper” that groups CPTcodes for office visits—such as 99210 through 99215).

A patient query engine 212 comprises computer readable instructions thatwhen executed on a processor retrieves patient data 514 of a patientprofile 511 using a patient UID 512, for example a query against thepatient profile database 510 and/or the EMR Database 520 of FIG. 5 . Thepatient query engine 212 may be utilized to add additionalpatient-related data to the utilization log 271 and/or the referral log231 such that prospective matching of a referral profile 430 may be madeagainst the patient-related data which may increase evaluation strength.The patient query engine 212 may, for example, retrieve the patient data256, including the demographic data 515, the coverage type 516, and/orthe diagnosis code 524. In one or more embodiments, the patient queryengine 212 may not extract or store individually identifiable healthinformation (e.g., which may be known as protected health information,or “PHI”), such that the logs of the log server 200 do not storesensitive and/or government-regulated personal information (e.g., underthe Health Insurance Portability and Accountability Act, or “HIPAA”). Inone or more other embodiments, the referral log 231 and/or theutilization log 271 may store PHI, including but not limited to thepatient UID 512, such that the referral log 231 and/or the utilizationlog 271 could be expanded with additional patient data 514 at a latertime, including in conjunction with a highly specialized instance of thereferral profile 430 specifying patient data 514 that is obtainable butnot ordinarily stored in the referral log 231 and/or the utilization log271. In one or more embodiments, a pseudonymous unique identifier may beemployed which may aid in associating one or more logs, as described inconjunction with the log association system 216. For example, thepatient UID 512 and/or the patient name 513 may be passed into a hashfunction (e.g., SHA-2) along with a nonce to generate a random and/orunpredictable string to be used as the pseudonymous unique identifier.Although not shown in the embodiment of FIG. 2 , the patient profile 511may also be stored on the log server 200.

A log storage module 214 comprises computer readable instructions thatwhen executed on a processor generates the referral log 231 and/or theutilization log 271 with extracted data from the referral record 504,the utilization record 502, and/or additional retrieved data (e.g., fromthe patient query engine 212). For example, the utilization log 271 cancomprise the provider UID 532 of a healthcare provider 102 (e.g., storedin a service provider ref 280 attribute), the place of service value250, the type of service value 252, and a utilization time 255associated with utilization of the healthcare services. The referral log231 and the utilization log 271 are described in detail below.

A log association system 216 comprises computer readable instructionsthat when executed on a processor: (i) determines a referral associationbetween a first referral record 504 and/or a first referral log 231 anda second referral record 504 and/or a second referral log 231; (ii)determines an association between a first utilization record 502 and/ora first utilization log 271 and a second utilization record 502 and/or asecond utilization log 271; and/or (iii) determines an associationbetween a utilization record 502 and/or a utilization log 271 and areferral record 504 and/or a referral log 231.

The log association system 216 may include an association detectionagent 218 comprising computer readable instructions that when executedon a processor determines generation of a log (e.g., the referral log231 and/or the utilization log 271) and compares the log to other logswithin the referral database 230 and/or the utilization database 270 forindicia of association. For example, in one or more embodiments anepisode of care may be assigned a unique identifier whereby a patient105 may be tracked through a health network. In such case the uniqueidentifier of the episode of care may be used to associate logs. In oneor more other embodiments, rules-based instructions may be provided, forexample determining that two instances of the referral log 231 are eachassociated with the patient UID 512 (and/or the pseudonymous uniqueidentifier, as described above) were issued within a finite time periodfrom the same referring provider. In another example, an association maybe determined between a utilization log 271 that resulted from ahealthcare provider 102B and a referral log 231 resulting from areferral of a patient 105 from a healthcare provider 102A to thehealthcare provider 102B. In this example, the association may bedetermined where: (i) a diagnosis code 524 (e.g., a code specifying hipreplacement surgery) in the utilization log 271 has a known associationto a type of service value 252 (e.g., orthopedic services) in thereferral log 231, (ii) where the demographic data 515 of the utilizationlog 271 and the demographic data 515 of the referral log 231substantially match (e.g., age, gender, ethnicity), and (iii) where boththe utilization log 271 and the referral log 231 are generated within afinite time period (e.g., less than one month, less than six months,less than two years).

The database association routine 220 comprises computer readableinstructions that when executed on a processor stores a databaseassociation (e.g., a pointer) associating and/or linking at least one of(i) a healthcare utilization log 271 with a referral log 231, and (ii) areferral log 231 (e.g., a referral log 231A) with a healthcare differentreferral log 231 (e.g., a referral log 231B). The database associationroutine 220 may define the relation data 260 of the referral log 231and/or the relation data 260 of the utilization log 271. The pointer maybe drawn to a referral log 231 by specifying the referral log UID 232 inthe referral log ref 262. The pointer may be drawn to a utilization log271 by specifying the utilization log UID 272 in the utilization log ref264. The database association and/or the pointer may be either a one-wayor two-way reference and/or link between logs. For example, a referrallog 231 may reference a utilization log 271 determined to be associated,but the utilization log 271 may not reference the referral log 231.

A referral database 230 comprises one or more stored referral logs 231.The referral log 231 may also be referred to as a healthcare referrallog 231. A referral log 231 comprises a number of attributes andassociated values and may be stored on the memory 203 and/or the storageof the log server 200 and/or additional servers. The referral log UID232 is data uniquely identifying the referral log 231 within thereferral database 230. The referring provider ref 240 is an attribute inwhich the healthcare provider 102 may be specified as the referringprovider. In one or more embodiments, the referring provider ref 240 mayspecify an individual provider ref 241 and a group provider 104 throughthe provider group ref 242. A data value of the referring provider ref240, the individual provider ref 241, and/or the provider group ref 242may be a provider UID 532.

Similarly, the referral provider ref 244 may specify the referringhealthcare provider 102. In one or more embodiments, the referralprovider ref 244 may specify an individual provider 103 through theindividual provider ref 245 and a group provider 104 through theprovider group ref 246. The value of the referral provider ref 244, theindividual provider ref 245, and/or the provider group ref 246 may be aprovider UID 532. An illustrative data structure that may be definedthrough the referring provider ref 240 and/or the referral provider ref244 is shown in conjunction with the embodiments of FIG. 10 and FIG. 16.

The type of service value 252 may be one or more values specifying oneor more types of healthcare service for which the referral was made. Forexample, the healthcare service may be broad (e.g., testing, surgery),general (e.g., oncology, medical imaging, trauma, orthopedic surgery),or specific (hip replacement surgery, dialysis services, one or moreanticipated procedure codes, etc.). A referral time 254 may be a timeand/or a date associated with the referral, for example when thereferral was made by the referring provider, timestamped by aninformation technology system, detected by the log server 200, and/oracknowledged by the referral provider.

The patient data 514 comprises data about the patient 105, which mayinclude demographic data 515, coverage type 516 (e.g., an insurancecompany and/or other payer covering expenses of the patient 105), one ormore instances of a diagnosis code 524, and/or other data retrieved fromthe patient profile 511 of the patient 105 and/or the EMR 521 of thepatient 105. Although not shown in the embodiment of FIG. 2 , thepatient data 514 may also include the patient UID 512, the patient name513, and/or the pseudonymous identifier.

The referral log 231 may comprise a relation data 260 relating thereferral log 231 to one or more other instances of the referral log 231and/or one or more other instances of the utilization log 271, as may bedefined by the log association system 216.

The log dataset 290 may comprise one or more of instances of thereferral log 231 (e.g., the referral log 231A through the referral log231N) that may be usable for referral evaluation such as by the logserver of FIG. 3 . In one or more embodiments, the log dataset 290includes every referral log 231 in the referral database 230. However,in one or more other embodiments, the log dataset 290 may be reduced toinclude less than all of the referral logs 231, for example where onlycertain instances of the healthcare provider 102 and/or certain timeperiods are to be evaluated. The log dataset 290 may also be utilized toreplicate data from the referral database 230 for portability and/orefficient processing, for example as may be transmitted to the referralserver 300 for analysis as shown and described in conjunction with theembodiment of FIG. 3 .

The utilization database 270 comprises one or more instances of theutilization log 271. The utilization log 271 may also be referred to asthe healthcare utilization log 271. A utilization log 271 comprises anumber of attributes and associated values and may be stored on thememory 203 and/or the storage of the log server 200 and/or additionalservers. The utilization log UID 272 is data that uniquely identifiesthe utilization log 271 within the utilization database 270.

The service provider ref 280 is an attribute in which the healthcareprovider 102 may be specified as the provider of the healthcareservices. In one or more embodiments, the service provider ref 280 mayspecify an individual provider ref 281 and a group provider ref 282. Thedata value of the service provider ref 280, the individual provider ref281, and/or the provider group ref 282 may be a provider UID 532.

A place of service value 250 (which may also be referred to and shown asthe POS value 250) may be one or more values specifying a locationand/or facility in which the healthcare service was performed, provided,and/or rendered. For example, the place of service value may be based onthe CMS POS code values, which may be a set of codes and/or locationdesignators maintained by CMS. However, one skilled in the art willrecognize that many other codes, values, and designations are possible,including but not limited to natural language descriptions of locationsand facilities.

The type of service value 252 may be one or more values specifying atype of healthcare service for which the referral was made, as describedin conjunction with the referral log 231, above. However, the type ofservice value 252 of the utilization log 271 may also include, forexample, procedure codes for procedures, treatments, and/or medicalservices actually performed. The patient data 514 comprises data aboutthe patient 105, as shown and described in conjunction with the referrallog 231, above. Although not shown in the embodiment of FIG. 2 , thepatient data 514 may also include the patient UID 512, the patient name513, and/or a pseudonymous identifier. The utilization log 271 maycomprise a relation data 260 relating the referral log 231 to one ormore other instances of the referral log 231 and/or one or more otherinstances of the utilization log 271, as may be defined by the logassociation system 216.

The log dataset 290 may comprise one or more instances of theutilization log 271 (e.g., the utilization log 271A through theutilization log 271N) that may be usable for referral evaluation. In oneor more embodiments, the log dataset 290 includes every instance of theutilization log 271 in the utilization database 270. However, in one ormore other embodiments, the log dataset 290 may be reduced to includeless than all of the utilization logs 271, for example, where onlycertain instances of the healthcare provider 102 and/or certain timeperiods are to be evaluated. The log dataset 290 may also be utilized toreplicate data from the utilization database 270 for portability andefficient processing, for example as may be transmitted to the referralserver 300 for analysis as shown and described in conjunction with theembodiment of FIG. 3 . Although not shown in the embodiment of FIG. 2 ,a hybrid dataset may include both a set of one or more instances of thereferral log 231 and a set of one or more instances of the utilizationlog 271.

Although not shown in the embodiment of FIG. 2 , additional databasesmay be stored on the log server 200 for ease of query in generating thereferral log 231 and/or the utilization log 271. For example, a providerdatabase may also be stored (e.g., similar to the provider database 530of FIG. 3 ).

FIG. 3 illustrates a referral server 300, according to one or moreembodiments. The referral server 300 includes a processor 301 that is acomputer processor and a memory 303 that is a computer memory (e.g.,RAM, solid state memory). Although not shown, the referral server 300may include storage (e.g., solid state storage, a hard disk, etc.).

A referral request agent 302 comprises computer readable instructionsthat when executed on a processor receives a referral request to refer apatient 105 for healthcare services (e.g., over the network 101). Thereferral request may be initiated by the computing device 400. Thereferral profile 430 may be generated on the computing device 400 andtransmitted to the referral server 300 (as shown in conjunction with theembodiment of FIG. 4 ), or selections made by the healthcare provider102 and/or a clinician 106 of the healthcare provider 102 to result inremote and/or server-side assembly of the referral profile 430 (e.g., onthe referral server 300).

A profile parsing routine 306 comprises computer readable instructionsthat validate the referral profile 430, add to the referral profile 430,and/or modify the referral profile 430. The profile parsing routine 306may also modify the referral profile 430, for example by expanding oneor more ranges of the referral profile 430 (e.g., the type of servicerange 452). The referral request may include a patient UID 512 and aninitial instance of the referral profile 430 with a type of servicerange 452. The patient UID 512 may then be used to query the patientprofile 511 to add a patient data range 456 to the referral profile 430and/or add additional data to the patient data range 456. In one or moreembodiments, the components of the patient data range 456 (e.g., thediagnosis code 524) may also be expanded to include additional ranges(e.g., a diagnosis code range that may include similar or relateddiagnosis codes relevant to the evaluation of value and/or access ofreferral providers).

A patient query engine 304 comprises computer readable instructions thatwhen executed on a processor queries a patient profile 511 of a patient105 and/or the EMR 521 of the patient 105 with the patient UID 512 ofthe patient 105 and then extracts a patient data 514. The patient data514 may comprise a demographic data 515 of the patient 105, a coveragetype of the patient 105, a diagnosis code 524 of the patient 105, and/orother data including but not limited to health data from the EMR 521.The patient data 514 may then be added to the referral profile 430 asthe patient data range 456. In one or more other embodiments, thepatient query engine 304 may also be present and/or execute on thecomputing device 400 where assembly of the referral profile 430 occurson the computing device 400.

The patient query engine 304 may be utilized to complete and/or augmentthe referral profile 430. For example, as generated by the computingdevice 400, the referral profile 430 may include a data placeholder fora designation of a coverage type 516 of the patient 105, but thecoverage type 516 may not be known by the clinician 106 (or even thepatient 105). The patient query engine 212 may then retrieve thecoverage type 516 to be added to the referral profile 430. What may be aseparate automated process, not shown in the embodiment of FIG. 3 , mayverify validity of the insurance coverage.

A profile matching engine 308 comprises computer readable instructionsthat when executed on a processor: (i) compares the referral profile 430to the set of referral logs 231 (e.g., in the log dataset 290) and tothe set of utilization logs 271 (e.g., which may also be included in thelog dataset 290), and (ii) determines matching instances of the set ofutilization logs 271 and/or the set of referral logs 231 to the referralprofile 430. In one or more embodiments, the referral profile 430specifies a type of service range 452 and a geospatial location of thepatient 105 (e.g., the location data 517 such as a residence, a streetaddress). In such example, each referral log 231 and/or each utilizationlog 271 will be determined to be a match if both (i) the type of servicevalue 252 is within the type of service range 452, and (ii) thehealthcare provider 102 acting as the referral provider (as specified inthe referral provider ref 244) and/or the service provider (as specifiedin the service provider ref 280) is within the geospatial area of thepatient 105.

The profile matching engine 308 may be exclusionary and/or haveadditional complex rules for determining relevant matches between thereferral profile 430 and the logs (e.g., the utilization log 271 and/orthe referral logs 231). For example, where the type of service range 452does not return enough matches (e.g., lower than a threshold statisticalsignificance), the type of service range 452 may be expanded to includesimilar healthcare services or more general categories of healthcareservices. The place of service range 450, a time range 454, and thepatient data range 456, as shown and described in FIG. 4 , may also besimilarly expanded. For example, where the demographic data 515 of thepatient data range 456 specifies a gender, the gender may be removedfrom the referral profile 430. In another example, a first instance of areferral log 231 and/or a utilization log 271 that matches the referralprofile 430 may also automatically generate a match for one or moreother logs with at least one database association to the first instance.Modification of matching is further shown and described in conjunctionwith the embodiments of FIG. 8 and FIG. 14 .

A dataset reduction sub-routine 310 comprises computer readableinstructions that when executed on a processor reduces the log dataset290 to the reduced dataset 390 and stores the reduced dataset 390,unique identifiers of each referral log 231 (e.g., the referral log UID232) and/or unique identifiers of each utilization log 271 within thereduced dataset 390 (e.g., the utilization log UID 272). The portion ofthe log dataset 290 retained in the reduced dataset 390 may be thematched portion determined by the profile matching engine 308.

A utilization rate routine 312 comprises computer readable instructionsthat when executed on a processor calculates, using the subset ofutilization logs 271A through 271Z in the reduced dataset 390, a placeof service utilization rate 332 (which may be shown and referred to asthe ‘POS utilization rate 332’) of each healthcare provider 102 actingas a referral provider referenced in a service provider ref 280 and foreach instance of the place of service value 250. In what may be a simpleexample, the reduced dataset 390 may comprise six instances of theutilization log 271, with four instances of the utilization log 271(e.g., the utilization log 271A through the utilization log 271D)associated with a first healthcare provider 102A providing services(e.g., specified through the service provider ref 280), and twoinstances of the utilization log 271 (e.g., the utilization log 271E andthe utilization log 271F) associated with a second healthcare provider102A providing services (e.g., specified through the service providerref 280). The utilization log 271A through the utilization log 271C mayhave a place of service value 250 specified by the CMS POS code ‘11’,which may designate an office, which may be a location, other than ahospital, skilled nursing facility (SNF), military treatment facility,community health center, state or local public health clinic, orintermediate care facility (ICF), where the health professionalroutinely provides health examinations, diagnosis, and treatment ofillness or injury on an ambulatory basis. The utilization log 271D mayhave a CMS POS code of ‘20’, which may designate an urgent carefacility, which may be a location, distinct from a hospital emergencyroom, an office, or a clinic, whose purpose is to diagnose and treatillness or injury for unscheduled, ambulatory patients seeking immediatemedical attention. The utilization log 271E may have a CMS POS code of‘11’, and the utilization log 271F may have a CMS POS code of ‘21’,which may designate an inpatient hospital, which may be a facility,other than psychiatric facility, which primarily provides diagnostic,therapeutic (both surgical and nonsurgical), and rehabilitation servicesby, or under, the supervision of physicians to patients admitted for avariety of medical conditions. In this example, the healthcare provider102A, for any type of service value 252 within the type of service range452 identified in the referral profile 430, would have a POS utilizationrate 336 of 75% for CMS POS code 11 and 25% for CMS POS code 20. Thehealthcare provider 102B, for any type of service value 252 within thetype of service range 452 identified in the referral profile 430, wouldhave a POS utilization rate 332 of 50% for CMS POS code 11, and 50% forCMS POS code 21. The POS utilization rate 332 of each healthcareprovider 102 may be stored in the referral evaluation data 330 inassociation with the provider UID 532. As shown and described inconjunction with the embodiment of FIG. 16 , the POS utilization rate332 may be calculated for each group provider 104 and for eachindividual provider 103 within the group provider 104. Although what maybe a simple example is provided, there may be hundreds, thousands, ormore instances of the healthcare provider 102 within the reduced dataset390, and each instance of the healthcare provider 102 may haveassociated hundreds, thousands, or more instances of the utilization log271.

A referral rate routine 314 comprises computer readable instructionsthat when executed on a processor calculates an inbound re-referral rate334 and/or a service-on-referral rate 336. The referral rate routine 314may comprise computer readable instructions that when executed on theprocessor calculates, using the subset of referral logs 231 within thereduced dataset 390, an inbound re-referral rate 334 of each referralhealthcare provider 102 referenced in a referring provider ref 240 andwithin a time period value 313. The inbound re-referral rate 334 is arate at which the referring healthcare provider 102 received a firstreferral and, within the time period value 313, the referring providerissued a second referral to a different referral provider, where thereferral log 231 of the first referral and the referral log 231 of thesecond referral are associated. For example, the referring provider mayissue the first referral to the first referral provider. The patient 105may then be denied service, may receive a price estimate that is toohigh, and/or the patient 105 may be advised that no appointments areavailable for a considerable time period (e.g., 2 weeks for an urgentmatter, 3 months for a non-urgent matter). The referring provider maythen need to re-refer the patient 105 one week later. In what may be asimple example, a healthcare provider 102A may refer a patient 105 to ahealthcare provider 102B, resulting in generation of a referral log231A. The time period value 313 may be two weeks. Twelve days later, thehealthcare provider 102A may refer the patient 105 to a healthcareprovider 102C, resulting in generation of a referral log 231B. Thereferral log 231A and the referral log 231B may be associated (e.g.,through the relation data 260). In the present example, the healthcareprovider 102A would have been subject to a re-referral to be included incalculating the inbound re-referral rate 334. Where the healthcareprovider 102A received eight referrals for a type of service value 252within the type of service range 452 of the referral profile 430, andthree of the referrals resulted in re-referrals by the referringprovider within the time period value 313, the inbound re-referral rate334 of the healthcare provider 102 would be calculated at 37.5%.

The inbound re-referral rate 334 may also be calculated with additionalinformation and nuance. In the example above, where the healthcareprovider 102A renders service resulting in a utilization log 271 that isassociated with the referral log 231A, and the utilization log 271occurs within a time period value 315 (e.g., one week, one month, sixmonths, two years which may be distinct from the time period value 313),the referral log 231B may indicate a second opinion or even a falsepositive and/or error in data resulting in generation of an inaccurateinstance of the referral log 231B. The time period value may be based onthe type of service value 252. The referral rate routine 314, therefore,references the time period value, determines the time associated withgeneration of the utilization log 271 is within the time period value,and reduces the inbound re-referral rate 334 accordingly. In the exampleabove, where the healthcare provider 102A received eight referrals inwhich three of the referrals resulted in re-referrals by the referringprovider within the time period value 313, but one such re-referralincluded an associated utilization record 502 and/or utilization log 271with a second time period value 315, the inbound re-referral rate 334 ofthe healthcare provider 102 would fall from 37.5% to 25% (twore-referrals out of eight total referrals matching the referral profile430). Alternatively or in addition, the time assessed may be a time ofthe actual healthcare utilization, e.g., a time associated withutilization of a healthcare server, as may be recorded in theutilization log 271.

The service-on-referral rate 336 may be the rate at which a healthcareprovider 102 directly provides the healthcare service for which thereferral was made, within a time period value 317 and for a type ofservice value 252 within the type of service range 452 of the referralprofile 430. For example, a healthcare provider 102 may, acting as areferral provider, receive three referrals from a referring providerresulting in generation of three instances of the referral log 231Athrough the referral log 231C. Two of the referred patients 105 mayeither be re-referred by the referral provider and/or not receivehealthcare services within the time period value. The third of thereferred patients 105 may be provided with healthcare services withinthe time period, resulting in a utilization log 271 that is associatedwith (e.g., through the relation data 260) one of the referral logs 231(e.g., the referral log 231C). In the present example, theservice-on-referral rate 336 of the healthcare provider 102 and withinall type of service values within the type of service range 452 would beapproximately 33.33%.

The referral evaluation data 330 may comprise the output data of theutilization rate routine 312 and/or the referral rate routine 314. Thereferral evaluation data 330 may include, for one or more instances ofthe healthcare provider 102A through the healthcare provider 102N (asidentified by the provider UID 532A through the provider UID 532N), thePOS utilization rate 332 (e.g., for each place of service value 250),the inbound re-referral rate 334, the service-on-referral rate 336,and/or other metrics and rates that may be indicative of value and/oraccess derivable from the utilization database 270 and/or the referraldatabase 230.

The utilization ruleset 316 comprises a set of rules stored in computermemory (e.g., the memory 303, the storage of the referral server 300)that when applied to the referral evaluation data 330 at least one ofscores, ranks, qualifies, and/or disqualifies one or more healthcareproviders 102 having a provider UID 532 in the referral evaluation data330 based on criteria comprising the POS utilization rate 332. Theutilization ruleset 316 may store and/or query data associating averagecost and/or service quality associated with each instance of the POSvalue 250. In what may be a simple example, an instance of a POS codevalue (e.g., CMS code ‘02’ for telemedicine) may be determined to behigh ranking, high scoring, and/or qualifying for the given type ofservice value 252 (e.g., as may be specified in the referral profile430). Continuing with the present example, each instance of the providerUID 532 within the referral evaluation data 330 may be ranked accordingto use of the CMS code value equal to ‘02’, with the highest utilizationrate ranked highest within the referral data 340. In another example,each provider UID 532 within the referral evaluation data 330 may score(e.g., on a scale of 1 to 5, 1 to 100, Excellent/Good/Fair/Poor, and/ora grade of A to F) based on use of the CMS code value equal to ‘02’.Although one place of service value 250 has been described, multipleplace of service values 250 may be used to generate the score, rank,qualification, and/or disqualification. For example, the provider UID532 may receive score points equal to the POS utilization rate 332 ofthe CMS value equal to ‘02’ multiplied by a constant ‘x’, added togetherwith score points equal to the POS utilization rate 332 of the CMS valueequal to ‘11’ multiplied by a different constant ‘y’. Ranking may alsobe made relative to statistical averages within the utilization database270. In one or more embodiments, a preferred instance of the place ofservice value 250 may be designed based on criteria comprising the typeof service value 252. For example, a given procedure may be specified tobe preferred to occur (e.g., based on other cost, value, and/or accessmetrics and priorities) at a certain CMS code value. A database may beused to define each of such preferred instances of the place of servicevalue 250. It should be noted that the utilization ruleset 316 may alsobe embodied in computer readable instructions.

A referral ruleset 318 comprises a set of rules stored in computermemory (e.g., the memory 303, the storage of the referral server 300)that when applied to the referral evaluation data 330 at least one ofscores, ranks, qualifies, and/or disqualifies one or more healthcareproviders 102 having a provider UID 532 in the referral evaluation data330 based on criteria comprising the inbound re-referral rate 334. Itshould be noted that the referral ruleset 318 may also be embodied incomputer readable instructions. In what may be a simple example, eachinstance of the healthcare provider UID 532 within the referralevaluation data 330 may be ranked according to the healthcare providerUID 532's associated inbound re-referral rate 334, with a lowest rate ofthe inbound re-referral rate 334 receiving the highest ranking. Inanother example, a score may be assigned (e.g., an inbound re-referralrate 334 of less than 10% receives a score of ‘5’ or ‘excellent’, ascore of between 10% but less than 25% receives a score of ‘4’ or‘good’, etc.). In another example, each healthcare provider UID 532 maybe qualified if it has a calculated instance of the inbound re-referralrate of less than 15%. In yet another example, each healthcare providerUID 532 may be disqualified if it has a calculated instance of theinbound re-referral rate 334 greater than 60%. In another example, eachhealthcare provider UID 532 may be first qualified, then scored, thenranked. Similarly, the service-on-referral rate 336 may also be scored,ranked, and/or used to qualify or disqualify the provider UID 532.

A hybrid ruleset 319 comprises a set of rules stored in computer memory(e.g., the memory 303, the storage of the referral server 300) that whenapplied to the referral evaluation data 330 at least one of scores,ranks, qualifies, and/or disqualifies one or more healthcare providers102 having a provider UID 532 in the referral evaluation data 330 basedon criteria comprising the POS utilization rate 332 and at least one ofthe inbound re-referral rate 334 and the service-on-referral rate 336.For example, an instance of the provider UID 532 may receive a firstcomponent of a score from the inbound re-referral rate 334 and a secondcomponent of the score from the POS utilization rate 332. Similarly,where a good inbound re-referral rate 334 would otherwise qualify theinstance of the provider UID 532, a poor POS utilization rate 332 wouldbe disqualifying. It should be noted that the hybrid ruleset 319 mayalso be embodied in computer readable instructions.

The referral ruleset 318, the utilization ruleset 316, and/or the hybridruleset 319 may also include additional rules that may effect incentivesor embody priorities of an operating organization of the referral server(e.g., a healthcare organization). For example, even where an instanceof the provider UID 532 may score highly and/or rank highly, additionalinformation about the provider UID 532 may otherwise be disqualifying(e.g., failure to adhere to guideline directed medical therapy).Conversely, where a provider UID 532 may rank poorly, or sufficient datafor a ranking and/or scoring may be lacking to be statisticallysignificant, the provider UID 532 may have score and/or rank improvementto ensure a reliable dataset is assembled over time. In one or moreembodiments, the qualification and/or ranking may be probabilistic tointroduce some randomness into an automated referral provider selectionprocess, e.g., a high score and/or qualification increases theprobability the provider UID 532 will be added to the referral data 340.In such case, indications of value and/or access may increase aprobability of being presented to a referring provider.

Although not shown in the embodiment of FIG. 3 , the provider UID 532may be further delineated and separately analyzed as a provider groupUID 534 and an individual provider UID 533. The referral ruleset 318,the utilization ruleset 316, and/or the hybrid ruleset 319 may beapplied for both a provider group UID 534 and/or an individual providerUID 533, with the results averaged or combined as a weighted average.For example, a healthcare provider 102 may be a group provider 104comprised of four instances of the individual provider 103, anindividual provider 103A through an individual provider 103D. The groupprovider 104 and each of the individual provider 103A through anindividual provider 103D may have a distinct inbound re-referral rate334, service-on-referral rate 336, and POS utilization rate 332 (e.g.,resulting in 5 distinctly calculated instances of each rate). The scoremay be a weighted score, where 50% of the score derives from a score ofthe provider group UID 534 over the past year, 12.5% of the score ofeach of the individual provider 103A through the individual provider103D in the previous six months.

A referral generation subroutine 320 comprises computer readableinstructions that when executed on a processor generates a referral data340 comprising each of one or more instances of the provider UID 532,the provider data 342 (which may include a name of a provider, a streetaddress, and/or other human-readable information), the score value 344,the rank value 346, and/or one or more aspects of the referralevaluation data 330 (e.g., such as the inbound re-referral rate 334, theservice-on-referral rate 336, and/or the POS utilization rate 332). Oneor more instances of the provider UID 532 may be deleted from thereferral evaluation data 330, for example due to disqualification. Thereferral generation subroutine 320 then transmits the referral data 340over the network 101 to the computing device 400.

A geographic reduction module 322 comprises computer readableinstructions that when executed on a processor extract from the patientprofile 511 a location data 517 (e.g., a street address, a geospatialcoordinate, a postal code) associated with the patient 105 anddetermines if one or more instances of the healthcare provider 102 arewithin a predetermined distance (e.g., 5 miles, the postal code, 100miles of the street address, etc.) based on the location data 517. Thelocation of the healthcare provider 102 may be based on a streetaddress, facility location, etc., as may be queried from the providerprofile 531. One or more instances of the provider UID 532 may befurther lowered in rank, lowered in score, provided with an “out ofrange” designation within the referral data 340, and/or disqualified andremoved from the referral data 340.

A referral completion module 324 comprises computer readableinstructions that when executed on a processor receive a selection of areferral provider from the computing device 400. A referral schedulingmodule 326 comprises computer readable instructions that when executedon a processor optionally schedules an appointment between the patient105 and the referral provider.

FIG. 4 illustrates a computing device 400, according to one or moreembodiments. The computing device 400 includes a processor 401 that is acomputer processor and a memory 403 that is a computer memory (e.g.,RAM, solid state memory). Although not shown, the computing device 400may include storage (e.g., solid state storage, a hard disk, etc.). Adisplay 401 (e.g., an LCD screen) may display a user interface 402(e.g., a graphical user interface). A point-of-care application 404(which may also be referred to as the POC application 404) may be acommercially available software program for treating, diagnosing, and/orotherwise providing healthcare services to a patient 105. The POCapplication 404 may provide a clinical documentation workflow 405 thatincludes a workflow for evaluating, diagnosing, documenting, creatingcare programs for, and otherwise addressing aspects of a healthcondition. The computing device 400 and/or the POC application 404 mayprovide the clinician 106 with a user interface 402 for viewing data ofan EMR of the patient 105 (e.g., the EMR 521 of FIG. 5 ), manipulatingthe clinical documentation workflow 405, and other healthcare functions.

A referral request routine 406 comprising computer readable instructionsthat when executed on a processor initiates a healthcare referral of apatient 105. The request may be initiated within a clinicaldocumentation workflow 405 of the POC application 404. For example, theclinical documentation workflow 405 may visually and/or graphicallyidentify a condition for which the patient 105 may need treatment. Wherea clinician 106 is unable to treat the condition of the patient 105, theclinician 106 may select an option within the user interface 402 torefer the patient 105.

A referral profile generation routine 408 comprises computer readableinstructions that when executed on a processor initiates the referralprofile 430 within a computer memory (e.g., the memory 303, the memory403), and stores one or more data ranges in the referral profile 430where the data ranges are defined automatically and/or throughrequesting manual input. For example, to the referral profile 430 may beautomatically and/or manually added a place of service range 450, a typeof service range 452, a time range 454, and/or a patient data range 456(which may further include a demographic data 515, a coverage type 516,and/or a diagnosis code 524). A manual input may include asking theclinician 106 and/or the patient 105 to input a data value. For example,the clinician 106 may be asked to select a type of service range 452from a drop-down menu. In another example, the patient 105 may be askedto provide (e.g., within a clinical care setting) one or more aspects ofthe patient data range 456. Although shown in the embodiment of FIG. 4 ,the referral profile 430 may be assembled on the computing device 400,the log server 200, and/or other computing devices and servers. Althoughnot shown in the embodiment of FIG. 4 , a patient query engine (e.g.,operating similarly to the patient query engine 304) may be utilized toquery and retrieve data for completing the patient data range 456 fromthe patient profile 511 and/or the EMR 521. The patient data range 456may help to determine logs and contextually determine value based on theneeds of the patient 105. Alternatively, or in addition, the patient UID512 may be submitted as part of the referral profile 430 (not shown inthe embodiment of FIG. 4 ) such that the referral profile 430 may becompleted, supplemented, and/or modified by the patient query engine304. The referral profile generation routine 408 may submit the fully orpartially assembled referral profile 430 over the network 101 to thereferral server 300.

The referral profile 430 includes a referral profile UID 432 and one ormore data ranges. A data range may include a place of service range 450,which may be, for example, one or more locations and/or facilities wherehealthcare services are performed. The place of service range 450 may bedesignated by one or more CMS POS code values. The place of servicerange 450 may also include specific locations, for example a specificlocation and/or facility where healthcare services are provided. Thedata range may include a type of service range 452, which may includebroad categories (e.g., oncology, geriatric medicine, other categorieswithin the Systematized Nomenclature of Medicine (SNOMED)), specifictreatments and procedures (e.g., radiation therapy), and/or a set of oneor more procedures (e.g., the CPT code values 77427, 77431, 77432,77435, 77469, and 77470 for radiation therapies), or a specificprocedure code value (e.g., CPT code value 77432). The time range 454may be selected and/or automatically assigned, for example based on thetype of service range 452. The time range 454 may be a period in whichthe set of referral logs 231 and/or the set of utilization logs 271 arerelevant. For example, the clinician 106 may be concerned with morerecent results (e.g., the last three months) rather than historicalvalue and/or access. On the other hand, it is also possible that thetime range 454 may be set by healthcare organization policy (e.g., 6months), and/or automatically assigned depending on other factors suchas the type of service range 452 (e.g., a longer period for cancertreatments, a shorter period for trauma injuries).

The data ranges may include the patient data range 456, which comprisesdata that may be used to further narrow matches within the log dataset290 based on characteristics of the patient 105. In one or moreembodiments, the patient data range 456 may include a demographic data515 such as age, gender, ethnicity, location, socio-economic status,employment, education information, income, and/or marital status. Thepatient data range 456 may include a coverage type 516 that may be aninsurance type or other cost coverage type of the patient 105, forexample an insurance plan ID, an insurance company name, etc. Thediagnosis code 524 may be a medical diagnosis code value (e.g., based onICD-9-CM, ICD-10, ICPC-2, NANDA, etc.). The patient data range 456 mayinclude many other aspects of additional information and may be drawnfrom the patient profile 511 and/or the EMR 521. As shown and describedherein, the referral profile 430 is submitted by the computing device400 over the network to the referral server 300.

The ranges specified in the referral profile 430 do not necessarilycorrespond to the exact patient data 514 and/or the exact healthcareservices the patient 105 may need. For example, the type of servicerange 452 may include services which are not needed by the patient 105but which are shown to be good indicators of value and/or access for thehealthcare service actually needed by the patient 105.

An integration routine 410 comprises computer readable instructions thatwhen executed on a processor receive the referral data 340 within theclinical documentation workflow 405 to provide actionable information tothe clinician 106 (e.g., as a clinician 106 of the healthcare provider102 acting as the referring provider) regarding value and/or access ofone or more potential referral providers. In the embodiment of FIG. 4 ,the referral data 340 is shown in a visual display format on the userinterface 402 (e.g., as opposed to a data structure format of FIG. 3 ).Specifically, in the present embodiment, a list of healthcare providers102 is displayed, each identified by the provider name 535A through theprovider name 535N, and each having provider data 342A through theprovider data 342N comprising additional information about the providersuch as location, accepted insurance, etc. The provider UID 532 may beincluded in the referral data 340 transmitted to the computing device400 (e.g., a provider UID 532A through a provider UID 532N), but may beretained in the memory 403 and not displayed to the clinician 106. Inthe embodiment of FIG. 4 , each instance of the healthcare provider 102represented within the referral data 340 may also include one or moreaspects of the referral evaluation data 330, in this case the rank value346 and the score value 344 associated with each instance of thehealthcare provider 102 represented (e.g., the rank value 346A is therank of the healthcare provider 102 identified by the provider name535A).

A referral selection routine 412 comprising computer readableinstructions that when executed on a processor receives a selection of ahealthcare provider 102 to act as a referral provider. The selection maybe made, for example, through the point-of-care application 404 by aclinician 106 of the healthcare provider 102 acting as the referringprovider. The selection may comprise the provider UID 532 of theselected referral provider. The selection may be forwarded through thenetwork 101 to the referral server 300 and/or another referralscheduling server.

FIG. 5 illustrates one or more record server(s) 500, according to one ormore embodiments. Although the embodiment of FIG. 5 shows distinctinstances of the record server(s) 500, two or more of the databases(e.g., the patient profile database 510, the EMR database 520, theprovider database 530, and/or the claims database 540) may be stored onthe same physical computing server. For example, the patient profiledatabase 510 and the provider database 530 may be stored on the sameserver of a healthcare organization. Each instance of the record serverincludes a computer processor 501 that is a computer processor and amemory 503 that is computer memory. Although not shown, the recordserver 500 may also include computer storage (e.g., a hard disk, a solidstate drive). Each instance of the record server 500 are communicativelycoupled to the network 101.

The patient profile database 510 comprises one or more instances of apatient profile 511. The patient profile 511 comprises a patient UID 512which may be a unique alphanumeric string through which the patient 105may be identified and/or the patient profile 511 addressed, a patientname 513, a coverage type 516 that is an insurance coverage type, ademographic data 515 (e.g., age, gender, ethnicity), and a location data517, which may be, for example, a geospatial coordinate, an address, astate, a region, and/or a postal code of a residence, a domicile, and/ora place of work, etc.

The EMR database 520 comprises one or more instances of the electronicmedical records 521, which may be referred to as the EMR 521. The EMR521 comprises an EMR UID 522 (which may also be a patient identifier), apatient data 514 which may include data about the patient 105, one ormore diagnosis codes 524, and/or additional data related to the patient105 such as clinical notes, test results, imaging results, reports,evaluations, etc. The EMR database 520 may work in connection with anEMR management application (not shown in the embodiment of FIG. 5 ), forexample, EPIC, Cerner, McKesson, All Scripts, and/or MedSys. One or morerecords within the EMR database 520 may be sufficient to act as theutilization record 502 and/or the referral record 504.

The provider database 530 comprises one or more instances of a providerprofile 531. The provider profile 531 comprises a provider UID 532. Inone or more embodiments, the provider profile 531 may be a profile for agroup provider 104 or an individual provider 103. Where the providerprofile 531 represents a group provider 104, the provider profile 531may include a reference to each of one or more individual providers533.1 through 533.n within (e.g., employed by, working for or under) theprovider group UID 534. In one or more embodiments, the provider groupUID 534 may utilize a tax identification number (TIN) and/or EIN issuedby the U.S. Internal Revenue Service. In one or more embodiments, theindividual provider UID 533 may be identified through a clinician numberor certification number (e.g., a National Provider Identifier (NPI),which may be a unique 10-digit identification number issued to healthcare providers in the United States by CMS). Although not shown in theembodiment of FIG. 5 , additional data related to the healthcareprovider 102 may be stored by the provider profile 531, for example theprovider data 342 that may be queried (e.g., by the referral server 300)and added to the referral data 340.

The claims database 540 comprises one or more instances of a healthcareclaim record 541. The healthcare claim record 541 may comprise a patientUID 512, a set of POS data 544 useable to determine one or more placesof service (e.g., and therefore one or more instances of the place ofservice value 250), a set of TOS data 546 useable to determine one ormore types of service (e.g., and therefore one or more instances of thetype of service value 252). The healthcare claim record 541 may includea provider UID 532 (including without limitation the provider group UID534 and/or the individual provider UID 533), one or more instances of adiagnosis code 524, a procedure code 548, and/or a coverage type 516.

FIG. 6 is a utilization log generation process flow 650, according toone or more embodiments. Operation 600 detects a utilization record 502(a record socumenting utilization of a healthcare service) of a firstpatient 105A. For example, a new entry in the EMR 521 of a patient 105may be stored, a new healthcare claim record 541 may be stored, and/oradditional data that may document the provision of healthcare servicesto the first patient 105 may be determined. Operation 602 extractsutilization data from the utilization record, for example the healthcareprovider 102 (as may be later stored in the service provider ref 280 ofthe utilization log 271), the utilization time 255, and/or one or moreaspects of the patient data 514. Operation 604 determines the place ofservice (POS) value 250 associated with the utilization record 604 (asmay be later stored in the POS value 250 of the utilization log 271).The place of service value 250 may be identified as a data field with atwo-digit CMS POS code value to be extracted from the utilization record502.

In another example, the place of service value 250 may be identified asa data field with one or more sub-facilities and/or facilitydifferentiators. For example, the place of service value 250 may be anacuity level (e.g., trauma center designation), an accreditation (e.g.,by a private and/or a non-profit organization or a state or a federalagency), a sub-facility having specialized equipment (e.g., an imagingcenter with an MRI accommodating oversized persons, an infectionpost-recovery room), and/or a facility funding profile (e.g., a facilitywhich receives federal funding).

Operation 606 determines a type of service associated with theutilization record. For example, operation 606 may extract a procedurecode 548 from a healthcare claim 541. In another example, operation 606may detect one or more predetermined terms, phrases, and/or synonymsfrom the Healthcare Common Procedure Coding System (HCPCS). In yetanother example, operation 606 may employ natural language processing.Operation 608 is a decision determining whether data of the firstpatient 105 should be gathered for inclusion in a utilization log 271(including without limitation the patient data 514, the patient name513, the coverage type 516, the demographic data 515, the location data517, and/or other information from the patient profile 511 and/or theEMR 521). Where data of the patient is to be included, operation 608proceeds to operation 610 which queries the patient profile of the firstpatient 105A (e.g., the patient profile 511) and/or the EMR 521 of thefirst patient 105A. Operation 612 extracts the patient data 514, andthen proceeds to operation 614. If no patient data 514 is to be includedat the decision of operation 608, operation 608 proceeds to operation614. Operation 614 generates a utilization log 271 comprising a providerUID 532, a place of service value 250, a type of service value 252,and/or the patient data (in such case the patient data was extracted andreturned in operation 612). Operation 614 may then end, or proceed tothe process flow of the embodiment of FIG. 7 .

FIG. 7 is a referral process generation process flow 750, according toone or more embodiments. Process flow 750 may begin at operation 700and/or continue operation 614 of process flow 650 of the embodiment ofFIG. 6 . Operation 700 initiates a referral request to refer a secondpatient 105B to a referral provider (e.g., a healthcare provider 102acting as a referral provider). For example, the initiation of thereferral request may occur on a computing device 400 through a POCapplication 404 where the initiator is a clinician 106 and/or through amobile application where the initiator is the patient 105.

Operation 702 generates a referral profile 430. At the time ofgeneration, the referral profile 430 may be assigned a unique identifier(e.g., the referral profile UID 432) and a set of memory addresses in acomputer memory to store additional data of the referral profile 430.Operation 702 may be initiated on a computing device 400, and/or on acomputing server (e.g., the referral server 300). Operation 704 selectsa type of service range 452 for addition to the referral profile 430.For example, a selection may be received by a trained clinician (e.g.,the clinician 106) who may select a healthcare service from a drop-downmenu, choose a procedure code from a searchable user interface window ornatural language search, etc. In one or more embodiments, the type ofservice range 452 may be automatically assigned based on activities ofthe clinician 106 within a clinical documentation workflow 405 of thePOC application 404. In the case where the computing device 400 is adevice of a patient 105, such as a smartphone, the patient 105 mayselect the type of service range 452. For example, the patient 105 mayselect the type of service range 452 with voice recognition interface, aquestionnaire leading the patient 105 to select a healthcare serviceand/or category of healthcare service with a high probability of beingcorrect, etc. Operation 706 selects a place of service range 450 to bestored in the referral profile 430. The place of service range 706 maybe two or more places of service, as may be designated with a value(such as a CMS POC code value). For example, the place of service range452 may specify CMS POC code values ‘11’ (office), ‘17’ (walk inclinics), ‘20’ (urgent care), ‘21’ (inpatient hospital), and ‘23’(emergency room). In one or more embodiments, all possible places ofservice (e.g., all CMS POC code values) may be automatically selected asthe place of service range 706.

Operation 708 is a decision determining whether a patient data range(e.g., the patient data range 456) should be included in the referralprofile 430. If the patient data range 456 is to be included, operation708 proceeds to operation 710. If not, operation 708 proceeds tooperation 714. Operation 710 queries the patient profile 511 of thesecond patient 105B and/or the EMR 521 of the second patient 105B andgathers data that may include a demographic data 515, a coverage type516, and/or a diagnosis code 524. Alternatively or in addition,operation 708 may initiate a manual entry process for requesting theclinician 106 and/or the patient 105 to assist in formulating thepatient data range 456. Operation 712 selects a patient data range 456and adds the patient data range 456 to the referral profile 430.Operation 714 stores the referral profile 430 in a computer memory.Although not shown in the embodiment of FIG. 7 , an additional processmay be the selection of a time range 454 (e.g., one week, six months,five years). Operation 714 may then end and/or proceed to operation 800of the embodiment of FIG. 8 .

FIG. 8 is a referral evaluation process flow 850, according to one ormore embodiments. Operation 800 compares a referral profile 430 to eachof one or more utilization logs 271 in a log dataset 290. The logdataset 290 may be stored in a utilization database 230 and/or may be adataset that is replicated out of one or more databases. Operation 802determines one or more of the utilization logs 271 match the referralprofile 430. In one or more embodiments, the match may need to be exactto be determined as a match. In what may be a simple example, a referralprofile 430 may comprise: (a) a type of service range 452 that includes(i) Brain and Neck MRI (e.g., a first instance of the type of servicevalue 252), and (ii) Brain CT (e.g., a second type of service value252); and (b) a patient data range 456 including a diagnosis code 524for a cerebral infraction. In such an example, an exact match would be autilization log 271 in which (a) a type of service value 252 of theutilization log 271 was either “Brain and Neck MRI” or “Brain CT,” and(b) the patient data 514 of the utilization log 271 included a diagnosiscode 524 for a cerebral infraction. Similarly, in one or moreembodiments, more specific matches may be permitted. In the exampleabove, an exact match could also occur where (a) the type of servicevalue 252 of the utilization log 271 was “70460” which may designate abrain CT scan with contrast dye, and (b) the patient data 514 of theutilization log 271 included a diagnosis code 524 of 2015/16 ICD-10-CM163.40 which may designate cerebral infarction due to embolism ofunspecified cerebral artery. However, in one or more embodiments, moregeneral matches may be permitted. In the example above, a match may bedetermined for a utilization log 271 with (a) any MRI or CT scanhealthcare service, and (b) any head or brain-related injury orcondition.

Operation 804 generates a reduced dataset (e.g., the reduced dataset390) comprising one or more instances of the utilization log 271 of thelog dataset 290. In one or more embodiments, each of the utilizationlogs 271 in the reduced dataset 390 may be compressed and/or haveextraneous data removed for portability and/or processing efficiency.Operation 806 is a decision for determining whether there are asufficient number of healthcare providers 102 represented in the reduceddataset 390. For example, a minimum threshold (e.g., three instances ofthe healthcare provider 102) may be required. In another example, theremay need to be a threshold number of healthcare providers 102 with asufficient data size for analysis (e.g., at least two providers eachhaving at least five matching instances of the utilization log 271).Other data size and sufficiency checks are also possible. If there are asufficient number of healthcare providers 102 within the reduced dataset390, operation 806 proceeds to operation 810. If not, operation 806proceeds to operation 808 which may automatically and/or manually expandthe referral profile 808 to be more inclusive (and/or reduce a matchingspecificity of operation 802). In the above example, the type of servicerange 452 may be generalized to “MRI healthcare services” and/or a matchmay be determined for a utilization log 271 with any healthcare servicerendered in association with an MRI. Operation 808 may then return tooperation 800.

Operation 810 calculates a POS utilization rate 332 for each instance ofthe healthcare provider 102 within the reduced dataset 390 (e.g.,represented in a utilization log 271). Continuing the above example, thePOS utilization rate 332 for a first healthcare provider 102A might be32% in an imaging and radiology center, 40% in a clinical office, and17% in a hospital, with 11% remaining distributed amongst othercategories. A second healthcare provider 102B may have the POSutilization rate 332 of 10% in an imaging and radiology center, 64% in aclinical office, and 5% in a hospital, with 21% remaining distributedamongst other categories.

Operation 812 applies a utilization ruleset 316 to score, rank, and/orqualify each instance of the healthcare provider 102 in the reduceddataset 390 based on criteria comprising the POS utilization rate 332. Ascore may be a letter score (e.g., A through F), a word score (e.g.,good/poor/fair), a number score (e.g., 1 to 100), and/or other scoringmethods. The rank may be based on the score, or may be based on the POSutilization rate 332 of one or more categories. For example, eachinstance of the healthcare provider 102 may be ordered (e.g., byordering each instance of the provider UID 532A within the referral data340) first by highest utilization of clinical offices, and second byimaging and radiology center. In one or more embodiments, size and/orquality of each type of facility may also be considered, for examplenumber of MRI machines, types of MRI capabilities, patient volume,number of annual procedures performed, etc. A qualification may be basedon certain thresholds (e.g., use of clinical offices greater than 25%,and/or employ Boolean logic (radiology imaging center user %+clinicaloffice use %>hospital use %).

It should be noted that facilities utilized by a healthcare provider 102may be context specific, for example where certain high-risk patients(e.g., who may be at risk for heart attack, stroke, anaphylaxis tocommon allergens, may be pregnant, etc.) may have procedures performedin a hospital. The referral profile 430 permits accommodation byselecting such factors as part of the matching criteria (e.g., thediagnosis code 524, which may be unrelated to the service which is beingreferred). Although not shown in the accompanying figures, an additionalrisk factor data range may also logged in the referral log 231 and/orthe utilization log 271 and be included in the referral profile 430.

Operation 814 generates a referral data 340 including the UIDs of eachpotential referral provider (e.g., each instance of the provider UID532). The referral data 340 may include additional data, as shown anddescribed in conjunction with the embodiment of FIG. 3 , including, forexample, the POS utilization rate 332. Operation 816 is a decision thatdetermines whether a sufficient number of healthcare providers 102 arewithin the ranking, qualifying, and/or scoring portion of the referraldata 340, and may otherwise operate similarly to operation 806. If asufficient number of healthcare providers score and/or qualify,operation 816 may proceed to operation 818, which may optionallygenerate an error notification (e.g., for a clinician 106 and/or anerror log for an automated system), then return to operation 808.Alternatively, although not shown in the embodiment of FIG. 8 ,operation 818 may proceed to an operation which reduces requirements(e.g., qualifications) under operation 812, and/or brings in externalfactors such as other performance indicators of each instance of thehealthcare provider 102. If a sufficient number of healthcare providers102 in operation 816 are determined, operation 816 proceeds to operation820 which transmits the referral data 340 to a computing device 400which may be running a POC application 404 over a network 101. Operation820 may then end, or proceed to the embodiment of FIG. 9 .

FIG. 9 is a referral selection process flow 950, which may begin atoperation 900 and/or may continue from the embodiment of FIG. 8 .Operation 900 receives a referral data 340 on a computing device 400running a point-of-care application 404. Operation 902 integrates thereferral data 340 in a clinical documentation workflow 405. For example,a user interface window within the clinical documentation workflow 405may be populated with the top three scoring instances of the healthcareprovider 102, with additional instances of healthcare providers 102within the referral data 340 accessible through a menu, button, orhyperlink. Operation 904 may provide an option to limit providers bygeolocation. When selected, operation 904 may proceed to operation 906which may query the patient profile (e.g., the patient profile 511) ofthe second patient 105B and retrieve a location data 517 (e.g., a streetaddress, a geospatial coordinate, a postal code) associated the withpatient 105. Operation 908 may then determine whether one or moreinstances of the healthcare provider 102 are within a predetermineddistance (e.g., 10 miles, 50 miles, 300 miles) based on the locationdata 517. For example, operation 908 may filter the one or moreinstances of the healthcare provider 102 in the referral data 340 bygeolocation (e.g., within a distance of the geolocation of the patient105, within a same postal code, etc.). It will be appreciated by oneskilled in the art of computer programming that geospatial filtering mayoccur in one or more other processes and/or process flows of the presentembodiments, and even may be a component of the referral profile 430(e.g., a location data range, not shown in the figures).

Operation 910 is a decision process that determines whether a sufficientnumber of instances of the healthcare provider 102 retained in thereferral data 340 remain within the geospatial constraint followingfiltering, for example at least three. If not, operation 910 maygenerate an error and/or return to operation 818 of FIG. 8 . Ifsufficient healthcare providers 102 do remain for selection, operation910 proceeds to operation 912. Similarly, where no geospatial limit isselected, operation 904 proceeds to operation 912.

Operation 912 receives a selection of a healthcare provider 102 to bethe referral provider. Operation 914 generates the referral selection asdata, for example by storing the provider UID 532. The provider UID 523may be transmitted over the network 101 to one or more servicesresponsible for processing and/or scheduling the referral. A referrallog 231 may also be generated in association with operation 914, asshown and described throughout this description.

FIG. 10 is a log data structure 1050, according to one or moreembodiments. In the embodiment of FIG. 10 , each square having roundedcorners models an instance of the healthcare provider 102A through thehealthcare provider 102F. The healthcare provider 102A may refer apatient 105A (not shown) to the healthcare provider 102B, as illustratedby the unidirectional solid arrowed line. The healthcare referral may bedocumented as the referral log 231B. Subsequently, the healthcareprovider 102B may provide services to the patient 105A, resulting ingeneration of the utilization log 271A. The referral log 231B and theutilization log 271A may be associated (e.g., by the log associationsystem 216 within the relation data 260 of the referral log 231A and/orthe relation data 260 of the utilization log 271A). The referral log231C and the referral log 231F may be associated by a databaseassociation 1000B. A rate in which a utilization log 271 becomesassociated with a referral log 231 may be an indicator of value and/oraccess. A time between (i) a referral time 254 of the healthcarereferral and/or a time of generation of the referral log 231A, and (ii)a utilization time 255 and/or generation of the utilization log 271A,may be assessed to determine responsiveness and wait time of thehealthcare provider 102B.

The healthcare provider 102A may also refer a patient 105B to thehealthcare provider 102C as a first healthcare referral along the dashedunidirectional arrowed line running from the healthcare provider 102A tothe healthcare provider 102C, resulting in generation of the referrallog 231C. In turn, the healthcare provider 102C may refer the patient105B to the healthcare provider 102F as a second healthcare referralalong the dashed unidirectional arrowed line running from the healthcareprovider 102C to the healthcare provider 102F, generating the referrallog 231F. A time between (i) a referral time 254 of the first healthcarereferral and/or a time of generation of the referral log 231C, and (ii)a referral time 254 of the second healthcare referral and/or a time ofgeneration of the referral log 231F, may be assessed to determineresponsiveness, wait time, or availability of the healthcare provider102C. This may be especially indicative where the type of service value252 is similar or identical in the referral log 231C and the referrallog 231F. The healthcare provider 102F may render the healthcareservices to the patient 105B, resulting in generation of the utilizationlog 271F which may then be associated to the referral log 231F throughthe database association 1000C and/or to the referral log 231C, eitherby way of the relation data 260 of the referral log 231F or through adirect reference (not shown in the embodiment of FIG. 10 ).

The healthcare provider 102A may also refer a patient 105C to thehealthcare provider as a first healthcare referral along the dot-dashedunidirectional arrowed line running from the healthcare provider 102A tothe healthcare provider 102D, resulting in generation of the referrallog 231D. The healthcare provider 102A may also refer the patient 105Cto the healthcare provider 102E along the dot-dashed unidirectionalarrowed line running from the healthcare provider 102A as a secondreferral to the healthcare provider 102E, resulting in generation of thereferral log 231E. In one or more embodiments, a referral pattern may beassessed as a re-referral event where (i) a threshold time is exceeded(e.g., 24 hours) between the referral time 254 of the referral log 231Dand the referral time 254 of the referral log 231E, which may indicateboth referrals were sequential, and (ii) a second threshold time is notexceeded (e.g., three months), which may indicate that both referralsare still within a same episode of care. In one or more embodiments,where the healthcare provider 102D renders a healthcare service to thepatient 105C, even if the referral log 231E has and/or will occur, thereferral log 231E in such instance may not be deemed a re-referral butanother service utilization (e.g., which may indicate a “secondopinion”). Additionally, a time between (i) a referral time 254 of thefirst healthcare referral and/or a time of generation of the referrallog 231D, and (ii) a referral time 254 of the second healthcare referraland/or a time of generation of the referral log 231E, may be assessed todetermine responsiveness, wait time, and/or availability of thehealthcare provider 102D.

In the embodiment of FIG. 10 , although a unidirectional arrow is shown,references may be drawn both backward or forward. For example, therelation data 260 of the referral log 231F may have a referral log ref262 drawing a reference to the referral log 231C, and the referral log231C may have a referral log ref 262 drawing a reference to the referrallog 231C. Each instance of the healthcare provider 102 shown in FIG. 10may be further modeled at both a group provider 104 level and anindividual provider 103 level, as shown and described in conjunctionwith the embodiment of FIG. 16 .

FIG. 11 is a referral log process flow 1150, according to one or moreembodiments. Operation 1100 detects a healthcare referral of a firstpatient 105 (e.g., a patient 105A). The healthcare referral may bedetected through identification of a referral record 504, as shown anddescribed in conjunction with the embodiment of FIG. 2 . For example, anew entry in the EMR 521 of a patient 105A may be stored, a newhealthcare claim record 541 may be stored, and/or there may be storedadditional documentary data from which the provision of healthcareservices to the patient 105A may be determined. A healthcare referralmay also be detected as a result of generating a referral through theprocess of FIG. 9 and/or FIG. 15 .

Operation 1102 determines a type of service (e.g., the type of servicevalue 252) associated with the healthcare referral 1102. Operation 1102extracts referral data from the referral record 504, for example thehealthcare provider 102 acting as the referring provider (as may belater stored in the referring provider ref 240 of the referral log 231).Operation 1102 may also extract the healthcare provider 102 acting asthe referral provider (as may be later stored in the referral providerref 244 of the referral log 231), the referral time 254, and/or one ormore aspects of the patient data 514. Operation 1104 determines the typeof service (TOS) associated with the utilization record 504 (as may belater stored in the POS value 250 of the referral log 231). For example,operation 1104 may extract a procedure code 548 from a healthcare claim541. In another example, operation 1104 may detect one or more naturallanguage terms from the Healthcare Common Procedure Coding System(HCPCS). In another example, natural language identification mayidentify one or more types of service that may be a general (e.g.,oncological) and/or more specific (e.g., radiation therapy), or evenwhat may be considered highly specific (stereotactic radiosurgicaldevice surgery, as may be known as Gamma Knife® surgery).

Operation 1106 is a decision determining whether patient data should begathered for inclusion in a referral log 231 (including withoutlimitation the patient data 514, the patient name 513, the coverage type516, the demographic data 515, the location data 517, and/or otherinformation from the patient profile 511 and/or the EMR 521). Wherepatient data 514 is to be included, operation 1106 proceeds to operation1110 which queries the patient profile of the first patient 105A (e.g.,the patient profile 511) and/or the EMR 521 of the first patient 105A.Operation 1110 then extracts the patient data, and then proceeds tooperation 1112. If no patient data 514 is to be included at the decisionof operation 1106, operation 1106 proceeds to operation 1112. In one ormore preferred embodiments, at least some patient data 514 may beappended to the referral log 231, however none is required. In one ormore embodiments, a patient UID 512 and/or pseudonymous identifier maybe added to the referral log 231 such that the patient data 514 may beadded and/or queried at a later time.

Operation 1112 generates a referral log 231 which may comprise a firstprovider UID 532 for the referring provider, a second provider UID 532for the referral provider, a type of service value 252, and/or thepatient data 514 of the first patient 105A (in such case the patientdata 514 was extracted and returned in operation 1110). Operation 1114may store the first referral log 231 in a referral database (e.g., thereferral database 230).

Operation 1112 may then end, or proceed to operation 1114, which maydetermine the first referral log 231 is associated with a secondreferral log 231, for example through use of the log association system216 of FIG. 2 . Operation 1118 may then associate the first referral log231 and the second referral log 231 within the referral database 230,for example by adding a one-way and/or two-way pointer in the firstreferral log 231 and the second referral log 231 (e.g., the firstreferral log 231 may link and/or draw an association to the secondreferral log 231 through the referral log ref 262 of the first referrallog 231 and/or the second referral log 231 may draw an association tothe first referral log 231 through the referral log ref 262 of thesecond referral log 231. Operation 1118 may then end, or may proceed tothe embodiment of FIG. 12 or the embodiment of FIG. 13 .

FIG. 12 is a utilization log association process flow 1250. Operation1200 determines storage of a utilization log 271 of a patient 105 (e.g.,utilization of healthcare services). Operation 1200 may be triggeredand/or execute, for example, upon new storage of the utilization log271. Alternatively or in addition, operation 1200 may determine storageof a preexisting instance of the healthcare utilization log 271.Operation 1202 determines the healthcare utilization log 1202 isassociated with a referral log 1202 (e.g., the first referral log 231 ofthe embodiment of FIG. 11 and/or the second referral log 231 of theembodiment of FIG. 11 ). Operation 1202, for example, may utilize thelog association system 216 of FIG. 2 . Operation 1204 associates thereferral log 231 and the healthcare utilization log 271 in a database(e.g., the referral database 230 and/or the utilization database 270).The association may be a one-way or two-way database association madethrough the relation data 260 of the referral log 231 and/or therelation data 260 of the utilization log 271.

FIG. 13 is a referral profile generation process flow 1350, according toone or more embodiments. Operation 1300 may start the process flow 1350and/or may be a continuation from the embodiment of FIG. 12 . Operation1300 initiates a referral request, and may be similar to operation 700of FIG. 7 . Operation 1302 initiates a referral profile (e.g., thereferral profile 430), and may be similar to operation 702. Operation1304 selects a type of service range (e.g., the type of service range452). The type of service may include one or more general categories ofmedical practice (e.g., pediatrics), general categories of healthcareservice (e.g., surgical procedures), general categories of procedure(e.g., appendectomy), basic procedure (e.g., procedure code value 44950for appendectomy), and/or a detailed procedure (procedure code value44960 for “appendectomy for ruptured appendix with abscess orgeneralized peritonitis”). Operation 1306 selects a place of servicerange (e.g., the place of service range 450), and may be similar tooperation 706.

Operation 1307 selects a time range (e.g., the time range 454). Forexample, a clinician 106 may choose (e.g., through the clinicaldocumentation workflow 405) and/or the healthcare organization mayautomatically preselect a time frame of one month, six months, one year,eight years, all of time for which logs are available, etc., as amatching criteria of the referral profile 430. In one or moreembodiments, the time range 454 may be selected based on the type ofservice range 452 selected (and/or even the patient data range 456selected in operation 1312).

Operation 1306 then proceeds to the decision of 1308, which, similar tooperation 708, determines whether a patient data range 456 should beselected. If a patient data range 456 is to be selected, operation 1306proceeds to operation 1310. If no aspect of the patient data range 456is to be included in the referral profile 430 beyond the data alreadyassembled, operation 1308 may proceed to operation 1314. Operation 1308,operation 1310, and operation 1312 may be similar to operation 708,operation 710, and operation 712, respectively. Operation 1314 storesthe referral profile (e.g., in the memory 403 of the computing device400 and/or the memory 303 of the referral server 300). Operation 1314may then end or proceed to the embodiment of FIG. 14 .

FIG. 14 is a referral evaluation process flow 1450, according to one ormore embodiments. Operation 1400 may begin the process flow 1450 and/ormay be a continuation of the process flow of FIG. 13 . Operation 1400compares the referral profile (e.g., the referral profile 430) to eachreferral log (e.g., the referral log 231) in a log dataset 290, wherethe log dataset includes one or more instances of the referral log 231(e.g., a referral log 231A through the referral log 231N). Operation1402 determines one or more instances of the referral log 231 that matchreferral profile 430. Operation 1402 may be similar to operation 802 ofFIG. 8 , except to match against the referral log 231 rather than theutilization log 271. In such case, a place of service value 250 or otherutilization-based matching criteria may not be used as matchingcriteria. For the time range 454, a match may be determined where thereferral time 254 is within the time range 454. For example, whereoperation 1402 executes on May 4, and the time range 454 is six months,a match between the referral time 254 and the time range 454 may occurwhere the referral time 254 specifies a time value on or before December4 of the previous year. Operation 1404, operation 1406, and operation1408 may function similarly to operation 804, operation 806, andoperation 808 of FIG. 8 , respectively. However, the reduced dataset 390will comprise one or more referral logs 231 (e.g., a referral log 231Athrough a referral log 231X) rather than (or in addition to) instancesof the utilization log 271.

Operation 1410 calculates, from each of the referral logs 231 and/orutilization logs 271 in the reduced dataset 390, an inbound re-referralrate 334 (abbreviated “re-referral rate” in the embodiment of FIG. 14 ),a service-on-referral rate 336 (abbreviated “service rate” in theembodiment of FIG. 14 ) and/or a service time (e.g., an average servicetime). The inbound re-referral rate 334 may calculate, for each referralprovider represented in the reduced dataset 390, a rate at which eachpatients 105 had to be re-referred by a corresponding referringprovider. In one or more embodiments, for example, an inboundre-referral may occur for the healthcare provider 102D of FIG. 10 upongeneration of the referral log 231E to the healthcare provider 102E. Theservice-on-referral rate 336 may be determined to be a percentage of thepatients 105 of a referral provider for which a utilization log 271 isgenerated which matches, substantially matches, and/or is otherwisedetermined to be associated with the referral log 231. For example, inone or more embodiments, the referral to healthcare provider 102B in theembodiment of FIG. 10 , including generation of the utilization log271A, may be considered an instance of service-on-referral. Similarly,the service time may be calculated by comparing the referral time 254 ofthe referral log 231A and the utilization log 271A, and an averageservice time calculated.

Operation 1411 may adjust the inbound re-referral rate 334 based on oneor more utilization logs 271. For example, returning to the example ofhealthcare provider 102D of FIG. 10 , if the healthcare provider 102Dwere to generate a utilization log 271 determined to be associated withthe referral log 231D within a threshold time period, generation of thereferral log 231E may be deemed not to contribute to theinbound-re-referral rate of the healthcare provider 102D. Conversely,although not shown in the embodiment of FIG. 14 , it is also possiblethat where (i) a subject referral provider receives a referral of apatient 105 referral resulting in generation of a referral log 231, (ii)a different healthcare provider 102 provides a healthcare service to thepatient 105 resulting in generation of a utilization log 271, and (iii)a match is determined between the referral log 231 and the utilizationlog 271, then the subject referral provider may, in one or moreembodiments, be deemed to be subject to an inbound re-referral.

Operation 1412 applies a referral ruleset 318 to score, rank, and/orqualify each instance of the healthcare provider 102 in the reduceddataset 390 based on criteria comprising the inbound re-referral rate334, the service-on-referral rate 336, and/or the service time (e.g., anaverage service time). A score may be a letter score (e.g., A throughF), a word score (e.g., good/poor/fair), a number score (e.g., 1 to100), and based on other scoring methods. The rank may be based on thescore, or may be based on a rate value (e.g., the inbound re-referralrate 334, the average service time) of one or more categories. Forexample, each instance of the healthcare provider 102 may be ordered(e.g., by ordering each instance of the provider UID 532A within thereferral data 340) first by lowest inbound re-referral rate 334 and thenby highest service-on-referral rate 336. In one or more embodiments,other factors may be considered such as time to re-referral (e.g., whichmay indicate the healthcare provider 102 that was acting as the referralprovider promptly informing the referring provider and/or the patient105 that no appointment was available). In one or more embodiments,multiple rates can be scored and combined to yield a final score basedon multiple factors, for example weighting the inbound re-referral rate334 as 50% of the score, weighting a POS utilization rate 332 as 25% ofthe score, weighting a service-on-referral rate as 12.5% of the score,and weighting an average service time as 12.5% of the score. Operation1414, operation 1416, operation 1418, and operation 1420 may functionsimilarly to operation 814, operation 816, operation 818, and operation820, respectively. However, operation 1420 may end and/or proceed to theembodiment of FIG. 15 . Although not shown in the embodiment of FIG. 14, operation 1420 may also proceed to the embodiment of FIG. 9 .

FIG. 15 is a patient selection process flow 1550. In the embodiment ofFIG. 15 , a patient 105 selects the referral provider, for example on acomputing device 400 such as a mobile device (e.g., a smartphone), atablet device, a notebook computer, and/or a desktop computer. Operation1500 may initiate the process flow of FIG. 15 , and/or continue theprocess flow of FIG. 15 and/or FIG. 8 . Operation 1500 receives thereferral data 340. For example, the referral data 340 may be receivedover the network 101 to a computing device 400 of the patient 105 (e.g.,continuing the example of FIG. 14 , the second patient 105).

Operation 1502 displays the referral data 340 through an application(e.g., a mobile app, a web browser application, a desktop application)running on the computing device 400. For example, the patient 105 mayhave initiated a referral request in operation 1300 of FIG. 13 throughthe application, defined one or more aspects of a referral profile 430,and then received the referral data 340 as a result. The referral data340 may be displayed in a window of the application such that thepatient 105 may easily see the name (e.g., the provider name 535), theprovider data 342 (including without limitation geographic location andinsurance acceptance information), and possibly one or more aspects ofthe referral evaluation data 330 (e.g., the score value 344, the rankvalue 346). Operation 1504 may receive a selection (e.g., from thepatient 105) to limit and/or organize the one or more healthcareproviders 102 within the referral data 340. For example, the patient 105may have access to a button or filtering option within the application.If limitation based on geolocation is selected, operation 1504 proceedsto operation 1506 where geolocation data is received. For example, thepatient 105 may be asked to manually provide geolocation data (e.g., thelocation data 517 such as a postal code, a street address, approvetransmission of a geospatial coordinate from a mobile device and/or aninternet protocol (IP) address location, etc.), and/or verifypre-populated data queried from the patient profile 511 (e.g., from thelocation data 517 which may be queried similar to the process ofoperation 906 of FIG. 9 ). Operation 1508 and operation 1510 may operatesimilar to operation 908 and operation 910, according to one or moreembodiments. However, operation 1510 may return to operation 1418 ofFIG. 14 where insufficient healthcare providers 102 remain to beselected.

Operation 1512 selects the referral provider, for example by receiving aclick, touch, and/or voice input from the patient 105 on the applicationrunning on the computing device 400. Operation 1514 generates thereferral selection as data, for example by storing the provider UID 532.The provider UID 523 may be transmitted over the network 101 to one ormore servers responsible for processing and/or scheduling the referral.A referral log 231 may also be generated in association with operation1514, as shown and described throughout this description. As a result,the patient 105 (who may be a consumer) may have selected a referralprovider with the benefit of support and/or analysis from the referralevaluation data 330. The referral provider may be alerted, a requestforwarded, and/or an appointment automatically scheduled.

FIG. 16 illustrates a log data structure 1650, according to one or moreembodiments. The embodiment of FIG. 16 illustrates the modeling andstorage of data within a data structure in which referral, utilization,and/or other relationships may be tracked and evaluated at asub-provider level which may increase accuracy and insight into providervalue and/or access. A healthcare provider 102 may be an individualprovider 103 and a group provider 104. The group provider 104 may betracked as a business entity (through a business entity ID issued by astate and/or by a business name), a unique taxpayer (e.g., through a taxidentification number (TIN) and/or employer identification number(EIN)), etc.), a tracking code assigned by a healthcare organization,and/or another form of unique identifier. The group provider 104comprises one or more individual providers 103. For example, the groupprovider 104B comprises ‘n’ individual providers referred to as theindividual provider 103B.1 through the individual provider 103B.n.Although the term “group” is used for the group provider 104, in one ormore embodiments the group size may be equal to one, as is shown withthe group provider 104A and the individual provider 103A.1. For example,such a group of one instance of the individual provider 103 may beimportant for tracking later additions to a medical practice initiatedby the individual provider 103. The individual provider 103 may betracked using a tracking ID issued by a government organization (e.g., aNational Provider Identifier (NPI), which may be a unique 10-digitidentification number issued to health care providers in the UnitedStates by CMS).

In the embodiment of FIG. 16 , the group provider 104A includes theindividual provider 103A.1. The individual provider 103A.1 may make areferral (e.g., through the continuous unidirectional solid line to thegroup provider 104B, and specifically to the individual provider 103B.2within the group provider 104B. The referral log 231A may result fromsuch a referral, including up to four documentable relationships: (i)the group provider 104A as a referring provider to the group provider104B as a referral provider; (ii) the group provider 104A as a referringprovider to the individual provider 103B.2 as a referral provider; (iii)the individual provider 103A.1 as the referring provider to theindividual provider 103B.2 as the referral provider; and (iv) theindividual provider 103A.1 as the referring provider to the groupprovider 104B as the referral provider. The individual provider ref 241and the provider group ref 242 may be stored as part of the referringprovider ref 240, and the individual provider ref 245 and the providergroup ref 246 may be stored as part of the referral provider ref 244, asshown and described in conjunction with FIG. 2 .

The utilization log 271 may be associated with both the group provider104B and the individual provider 103B.1, as shown by the databaseassociation 1000A and the database association 1000B in FIG. 16 andwhich may be stored using the individual provider ref 281 and theprovider group ref 282 of FIG. 2 . Similarly, the utilization log 271may be associated with the referral log 231A through the databaseassociation 1000C.

Each instance of the healthcare provider 102 may result in evaluatedrates and separate but what may be related healthcare value and/oraccess. For example, a group provider 104 may be responsive andavailable, while an individual provider 103 may not, and vice-versa.Similarly, an individual provider 103 may move between groups (e.g., behired by a group, found a new group, etc.). In one or more embodiments,therefore, a measurement of both individual providers 103 and groupproviders 104 may be combined for what may be more accurate andinsightful data related to value and/or access. For example, an inboundre-referral rate 334, a service-on-referral rate 336, and/or a POSutilization rate 332 may be calculated for both a group provider 104 andfor an individual provider 103 within the group provider 104. Theresults may be combined, weighted and combined, or provide additionalfactors for application of the utilization ruleset 316 and/or thereferral ruleset 318. For example, qualification for inclusion in thereferral data 340 may require consistency between individual providers103 within a group provider 104, for example, such that no individualprovider 103 has a POS utilization rate 332 for hospitals that exceedsmore than twice that of other individual providers 103 within the sameinstance of the group provider 104. Other data that is possiblyinsightful may also be identified. For example, where the referral wasmade to the individual provider 103B.2, the individual provider 103B.1appears to have performed the associated healthcare service, which mayindicate time and/or administrative resources may be saved in directreferral to the individual provider 103B.1 within the same groupprovider 104B. In the embodiment of FIG. 16 , an inbound re-referral isalso illustrated through the four unidirectional dashed arrowed lines.If determined to be an inbound referral qualified to increase an inboundre-referral rate 334, the inbound re-referral rate may increase for boththe individual provider 103B.2 and for the group provider 104B. Whereboth the individual provider 103 and the group provider 104 isevaluated, they may be included as distinct entities within the referraldata 340, or may be collapsed into a single representation for purposesof the clinician 106 and/or the patient 105 selecting a referralprovider. The representation may generally be either the group provider104 or the individual provider 103 within the group provider 104.Details distinguishing the group provider 104 and the individualprovider 103, and their respective score, rank, and/or qualification,may be available through a user interface by selecting an option formore information.

FIG. 17 is a provider evaluation process flow 1750, according to one ormore embodiments. Operation 1700 calculates a POS utilization rate 332and/or an inbound re-referral rate 334 of one or more individualproviders 103 represented within the reduced dataset 390. An individualprovider 103, as shown and described herein, may be represented whereone of more of the utilization logs 271 and/or one or more of thereferral logs 231 include a reference to the individual provider 103within the individual provider ref 245 of a referral log 231 and/or theindividual provider ref 281 of the utilization log 271. Operation 1702calculates a POS utilization rate 332 and/or an inbound re-referral rate334 of one or more group providers 104 represented within the reduceddataset 390. A group provider 104, as shown and described herein, may berepresented where one of more of the utilization logs 271 and/or one ormore of the referral logs 231 include a reference to the group provider104 within the provider group ref 246 of a referral log 231 and/or theprovider group ref 282 of the utilization log 271. The POS utilizationrate 332 and the inbound re-referral rate 334 of both the individualprovider 103 and of the group provider 104 may be stored under discreteinstances of the provider UID 532 within the referral evaluation data330, e.g., a provider group UID 534 and an individual provider UID 533.

Operation 1704 applies a weighted average of group provider 104 andindividual provider 103 rates (e.g., the POS utilization rate 332 and/oran inbound re-referral rate 334). For example, a weighted instance ofthe POS utilization rate 332 on which a score and/or rank may be basedmay be weighted such that 60% comes from the group provider 104receiving the referral (e.g., in the embodiment of FIG. 16 , the solidunidirectional arrow running from the group provider 104A and/or theindividual provider 103A.1 to the group provider 104B) and 40% from thecomes from the individual provider 103 receiving the referral (e.g., inthe embodiment of FIG. 16 , the individual provider 103B.1 associatedwith the utilization log 271 of FIG. 16 ).

Operation 1706 applies a referral ruleset to score, rank, and/or qualifyhealthcare providers 102. The referral ruleset may be the utilizationruleset 316, the referral ruleset 318, and/or the hybrid ruleset 319.The referral ruleset may score based on both the POS utilization rate332 and/or the inbound re-referral rate 334. For example, the referralruleset may award a score to each group provider 104 based on an inboundre-referral rate 334 of each of the individual providers 103 of thegroup provider 104. In another example, the referral ruleset may rankprimarily by POS utilization rate 332 of the group provider 104, andsecondarily by an average of the inbound re-referral rates 334 of eachindividual provider 103 within the group provider 104. In yet anotherexample, the group providers 104 may be ranked according to the inboundre-referral rate of the group provider 104, but may be disqualified orreduced in rank if any individual provider 103 within the group provider104 has an inbound rereferral rate exceeding a threshold percentage suchas 75%. One skilled in the art will recognize additional possibilitiesfor score, rank, and/or qualification. In one or more embodiments,operation 1704 may be removed and any weighting completed by thereferral ruleset of operation 1706.

Operation 1708 generates a referral data 340 including UIDs of one ormore potential referral providers, e.g., the provider UID 532. Eachinstance of the individual provider 103 and each instance of the groupprovider 104 may be included as distinct entities within the referraldata 340, or may be collapsed into a single representation for purposesof human identification and selection, as described in conjunction withthe embodiment of FIG. 16 . In one or more embodiments, therepresentation is either a group provider 104 or the individual provider103. In one or more other embodiments, the group provider 104 may beprominently identified, with the individual provider 103 within thegroup provider 104 also listed with less visual emphasis. Therepresentation may also differ based on whether the referral data 340 isto be presented to a clinician 106 (e.g., which may wish for moreinformation on the group provider 104) or the patient 105 (e.g., whichmay wish for more emphasis on the individual provider 103, including aprofile photo of each clinician 106 that may be the individual provider103).

FIG. 18 illustrates an example of generation of a referral data based ona patient profile matched to logs of a reduced dataset, according to oneor more embodiments. As previously shown and described, a patient 105may engage in a native encounter with the clinician 106, who may be thehealthcare provider 102A. The clinician 106 may wish to refer thepatient 105 to a different healthcare provider to receive a servicewhich the clinician 105 is unable to provide.

The referral profile 430 for the patient 105 is generated on thecomputing device 400 and/or one or more server computers (e.g., thereferral server 300), and may be synthesized with data including bothmanual input data from the patient 105 or clinician 106 and/orautomatically retrieved data of the patient 105 (e.g., from the patientprofile 511). After submission to the referral server 300, the referralserver 300 may generate the reduced dataset 390 according to one or moreprocesses shown and described herein. Although five referral logs 231Athrough referral log 231E and six utilization logs 271A thoroughutilization log 271F are shown, the reduced dataset 390 may include manymore instances of the referral log 231 and/or instances of theutilization log 271.

The healthcare provider 102B in the present example is “Parity Health”,and may be represented (e.g., identified with a provider groupidentifier 534) in three instances of the utilization log 271 (theutilization log 271A and the utilization log 271B) and as the referralprovider in two instances of the referral log 231 (the referral log 231Athrough the referral log 231C). The relation may occur through adatabase association (e.g., a database association 1000A) between theprovider profile 531 and each of the logs. The referral profile 430 isshown associated through a determined match (e.g., determined by theprofile matching engine 308), shown in FIG. 18 as the match 1800.

Similarly, the healthcare provider 102C may be the practice of Dr. ReeseGoodwin, and may be represented with a total of six combined utilizationlogs 271 (the utilization log 271C through the utilization log 271F) andreferral logs 231 (the referral log 231C and the referral log 231D). Forclarity, the utilization logs 271 and the referral logs 231 associatedwith Powhatan Healthcare (e.g., a healthcare provider 102D), andSafehands Surgical (e.g., a healthcare provider 102E) are not shown inthe embodiment of FIG. 18 .

Following application of the utilization ruleset 316 and/or the referralruleset 318, as described herein, each of the healthcare providers 102are scored and/or ranked. The referral data 340 is generated as anoutput (e.g., by computer readable instructions of the referral server300). Illustrated in what may be a simple example for clarity, thereferral data 340 may include a provider name 535, a rank value 346, anda score value 344. Parity Health scores the highest, at 89.42. and isranked highest by simple ordering of the score in the present example.Additional information, warnings, or analysis may also be presented. Forexample, the offices of Dr. Reese Goodwin may be noted to be within ageospatial radius of the healthcare provider 102A and/or an addressesassociated with the patient 105. In another example, a reason forSafehand Surgical's lower score may be a relatively poor POS utilizationrate 332, even where the Inbound re-referral rate 334 may be high(“Warning: inpatient surgeries significantly above average withinhealthcare providers having data matching referral profile”). The reasonmay be part of the access evaluation data 330, processed to be made intoan easily readable form from raw data.

Another example embodiment will now be provided in which a healthnetwork operates one or more components of the referral evaluationnetwork 100. The health network in this example is an ACO referred to inthis example as “Progressive Network” having within the network ahealthcare provider 102 referred to as “Longevity Health”.

A patient 105 (Abe) may engage in a native encounter with LongevityHealth, and specifically a native encounter with an instance of theclinician 106 named Beth. Beth is a primary care physician performingservices within a group practice of physicians at Longevity Healthfocusing exclusively in primary care and internal medicine. Beth worksin an office downtown two days per week; the rest of the week, Bethworks in the suburbs.

Beth enters the exam room and greets Abe, a 55-year-old man. Beth logsin to a point of care application (e.g., the POC application 404)running on Beth's iPad (e.g., the computing device 400). Beth retrieveshealth information of Abe's electronic medical record (e.g., from theEMR database 520), including demographic data 515 about Abe—such as ageand weight, Abe's health coverage (e.g., the coverage type 516), andAbe's current diagnoses, which may include one or more diagnosis codes524. Beth briefly reviews the electronic medical record and notices thatAbe has lost a significant amount of weight since his last appointment.After her review, Beth asks Abe why Abe came today. Abe explains that hehas had trouble with bowel movements recently. Abe reports that he ischronically constipated, and that his bowel movements are painful. Abealso reports that he has noticed blood in his stool. Abe otherwise sayshe feels healthy. Beth enters this information into the electronicmedical record (e.g., to be stored in the EMR database 520).

Based on these reported symptoms, Abe's age, and Abe's recent weightloss, Beth fears Abe may have colon cancer. Beth decides that Abe shouldundergo a colonoscopy to diagnose the condition or disease that may becausing Abe's symptoms. Beth is not a gastroenterologist and knows sheshould not perform the colonoscopy herself or otherwise attempt todiagnose the condition. Beth therefore wishes to find a referralprovider, a gastroenterologist, to whom Beth will refer Abe for thecolonoscopy.

While still in the exam room with Abe, Beth uses the user interface ofher iPad to initiate a referral, activating a referral request routine410 running as part of a software application. Beth enters informationnecessary about the referral (such as the code for “colonoscopy” in thetype of service range), which, when combined with other informationpulled from Abe's EMR 521, (such as the patient data 514 extracted fromthe electronic medical record database 520, and optionally thegeolocation of Beth's downtown office), will generate a referral profile430. To complete the referral profile 430, Beth may also indicate thatthe preferred place of service is a physician office, since Abe isotherwise generally healthy and does not need to undergo the colonoscopyin a setting (such as a hospital operating room) with resourcesnecessary to serve a higher-risk patient. The selection of a preferredplace of service may define the place of service range 450 of thereferral profile 430. For example, the referral profile may include atype of service range 452 that includes “colonoscopy”, a patient datarange 456 that may include Abe's gender and an age group (e.g., 55 to 60years of age), a coverage type (e.g., HMO, and/or a specific plan suchas Blue Cross/Blue Shield®), and a diagnosis code 524. It may bedetermined a diagnosis code 524 is relevant or irrelevant to referredservice. For example, a lookup table may include a list of conditionsknown to create a high risk of infection, cardiac arrest, or otherconditions or complications. However, in the present example Abe doesnot have any such diagnoses, and so the diagnosis code 524 field may beunspecified. The referral profile 430 is submitted by Beth, and travelsover the internet to a server computer (e.g., referral server 300).

A profile matching engine 308 determines utilization logs 271 and/orreferral logs 231 that match the referral profile 430. For example, autilization log 271 may be a match where a healthcare provider 102provided a colonoscopy to a different patient 105 (“Calvin”), whereCalvin was male, within the age of 55 to 60 at the time of treatment,and had the same or a similar coverage type at the time of treatment(and/or in which the healthcare provider 102 is known to currentlyaccept the same or a similar coverage type). A similar matching mayoccur for referral logs 231.

A dataset reduction subroutine 310 generate a reduced dataset 390, asshown and described in one or more of the present embodiments. In thisexample, the reduced dataset 390 includes both a number of utilizationlogs 271 and a number of referral logs 231.

The referral evaluation data 330 generated as an output includesutilization logs 271 and/or referral logs 231 having the followinghealthcare providers 102 represented in the reduced dataset 390: (1)Metropolitan Cancer Center (a downtown hospital with a gastroenterologydepartment); (2) Suburban Gastroenterology (a group ofgastroenterologists working in the suburbs); (3) Cameron, M.D (agastroenterologist working downtown as a solo practitioner); (4) Daniel,M.D. (a gastroenterologist working downtown in a group practice withother gastroenterologists); and (5) Eastern Gastroenterology Center (afreestanding gastroenterology clinic that performs colonoscopies in anoffice setting).

The referral rate routine 314 applies the referral ruleset 318 to thereduced dataset 390, in the present example to both score and then rankby score the referral providers that are likely available to receive thereferral of Abe's colonoscopy.

Eastern Gastroenterology Center is excluded because EasternGastroenterology Center does not accept Abe's health insurance.Metropolitan Cancer Center is excluded because the utilization records271 of service provided by the Metropolitan Cancer Center indicate thatthe place of service range 450 is generally “hospital” for colonoscopies(e.g., above a certain threshold, such as 50%). That is, a predominateusage of “hospital” as a place of service does not match the referralprofile 430 for Abe's colonoscopy, or may otherwise be specified by theutilization ruleset 316 as disqualifying. For example, ProgressiveNetwork may further disqualify or lower any score of Metropolitan CancerCenter because it also performs other diagnostic procedures (e.g.,unrelated to colonoscopies) in a hospital setting at a rate above anaverage rate of healthcare providers within Progressive Network. Theusage of a hospital in the utilization record 271 may be specified byCMS POS code 21 and/or by a natural language tagging.

The referral logs 231 and utilization records 271 indicate that SuburbanGastroenterology, Cameron, and Daniel all accept Abe's insurance and allgenerally have predominate place of service for colonoscopies of“physician office”, as may be specified by HMS POS code 11 or by anatural language tagging.

In the present example, geolocation may also be a part of referralprofile 430, rather than a subsequent filter applied to results.Suburban Gastroenterology may therefore be removed because thegeolocation of Suburban Gastroenterology is outside the geographic areato which Beth would refer a patient 105 (such as Abe) to be seen (e.g.,outside the zip code, outside city, a greater distance than 40 miles,etc.).

The referral server 300 in the present example then scores Cameron andDaniel based on additional access to care, using the referral ruleset toevaluate re-referral data and service-on-referral data stored inreferral logs 231 for both referral providers. Daniel receives a lowscore because Longevity Health and/or other referring providers haveseveral referral logs 231 without matching utilization logs 271 toDaniel. Moreover, the referral logs 231 indicate that, for instances ofpatients 105 referred for gastroenterology procedures to Daniel, many ofthose patients 105 have been the subject of a re-referral for the samegastroenterology procedure (which may indicate that Daniel does notpromptly perform referred gastroenterology procedures, including forreferrals by Beth and Longevity Health). In contrast, Cameron mayreceive a relatively high score because utilization logs 271 andassociated referral logs 231 indicate that Cameron has a low rate ofre-referral, and because the utilization logs 271 and/or utilizationrecords show that colonoscopies referred to Cameron are generatedpromptly after receipt.

A ranked list of the qualifying referral providers, in this case Danieland Cameron, are then transmitted over the network 101 to the computingdevice 400 (e.g., as part of the referral data 340), which is thendisplayed to Beth in the user interface 402 of the point-of-careapplication 404, along with the score for each referral provider (e.g.,3.6 out of 5 for Daniel, 4.8 out of 5 for Cameron).

From the perspective of Beth, the above referral profile submission, logmatching, and referral provider scoring, ranking, and presentation mayoccur almost instantaneously with on-demand processing. Beth thenselects Cameron as the referral for Abe's colonoscopy. The referralinformation is transmitted to Cameron (e.g., Abe's name and relevantdetails), and a referral log (e.g., the referral log 231) may also begenerated to document the referral.

In the present example, Beth efficiently referred Abe in a short periodof time and with a data backing her decision. Abe benefits by having amaximized probability of success based on existing data and hisindividual needs which may be incorporated into the referral profile430. A maximized positive outcome will bring goodwill and patientsatisfaction to Longevity Health and Progressive Network, while alsolowering the risk of malpractice or bad patient outcome due to anunsuccessful or inefficient handoff between one healthcare provider andanother. In addition, Progressive Network may gain visibility into thereferral process and be able to enforce its policies and createincentives through the referral evaluation network 100 that improvesefficiencies within the network.

Although the present embodiments have been described with reference tospecific example embodiments, it will be evident that variousmodifications and changes may be made to these embodiments withoutdeparting from the broader spirit and scope of the various embodiments.For example, the various devices, engines, agent, routines, and modulesdescribed herein may be enabled and operated using hardware circuitry(e.g., CMOS based logic circuitry), firmware, software, or anycombination of hardware, firmware, and software (e.g., embodied in anon-transitory machine-readable medium). For example, the variouselectrical structure and methods may be embodied using transistors,logic gates, and electrical circuits (e.g., application specificintegrated circuitry (ASIC) and/or Digital Signal Processor (DSP)circuitry).

In addition, it will be appreciated that the various operations,processes, and methods disclosed herein may be embodied in anon-transitory machine-readable medium and/or a machine-accessiblemedium compatible with a data processing system (e.g., the log server200, the referral server 300, the computing device 400, the recordserver 500). Accordingly, the specification and drawings are to beregarded in an illustrative rather than a restrictive sense.

The structures in the figures such as the engines, routines, and modulesmay be shown as distinct and communicating with only a few specificstructures and not others. The structures may be merged with each other,may perform overlapping functions, and may communicate with otherstructures not shown to be connected in the figures. Accordingly, thespecification and/or drawings may be regarded in an illustrative ratherthan a restrictive sense.

In addition, the logic flows depicted in the figures do not require theparticular order shown, or sequential order, to achieve desirableresults. In addition, other steps may be provided, or steps may beeliminated, from the described flows, and other components may be addedto, or removed from, the described systems. Accordingly, otherembodiments are within the scope of the preceding disclosure.

Embodiments of the invention are discussed above with reference to theFigures. However, those skilled in the art will readily appreciate thatthe detailed description given herein with respect to these figures isfor explanatory purposes as the invention extends beyond these limitedembodiments. For example, it should be appreciated that those skilled inthe art will, in light of the teachings of the present invention,recognize a multiplicity of alternate and suitable approaches, dependingupon the needs of the particular application, to implement thefunctionality of any given detail described herein, beyond theparticular implementation choices in the following embodiments describedand shown. That is, there are modifications and variations of theinvention that are too numerous to be listed but that all fit within thescope of the invention. Also, singular words should be read as pluraland vice versa and masculine as feminine and vice versa, whereappropriate, and alternative embodiments do not necessarily imply thatthe two are mutually exclusive.

Unless defined otherwise, all technical and scientific terms used hereinhave the same meanings as commonly understood by one of ordinary skillin the art to which this invention belongs. Preferred methods,techniques, devices, and materials are described, although any methods,techniques, devices, or materials similar or equivalent to thosedescribed herein may be used in the practice or testing of the presentinvention. Structures described herein are to be understood also torefer to functional equivalents of such structures.

From reading the present disclosure, other variations and modificationswill be apparent to persons skilled in the art. Such variations andmodifications may involve equivalent and other features which arealready known in the art, and which may be used instead of or inaddition to features already described herein.

Although claims have been formulated in this application to particularcombinations of features, it should be understood that the scope of thedisclosure of the present invention also includes any novel feature orany novel combination of features disclosed herein either explicitly orimplicitly or any generalization thereof, whether or not it relates tothe same invention as presently claimed in any claim and whether or notit mitigates any or all of the same technical problems.

Features which are described in the context of separate embodiments mayalso be provided in combination in a single embodiment. Conversely,various features which are, for brevity, described in the context of asingle embodiment, may also be provided separately or in any suitablesub-combination. The applicants hereby give notice that new claims maybe formulated to such features and/or combinations of such featuresduring the prosecution of the present application or of any furtherapplication derived therefrom.

References to “one embodiment,” “an embodiment,” “example embodiment,”“various embodiments,” “one or more embodiments,” etc., may indicatethat the embodiment(s) of the invention so described may include aparticular feature, structure, or characteristic, but not every possibleembodiment of the invention necessarily includes the particular feature,structure, or characteristic. Further, repeated use of the phrase “inone embodiment,” or “in an exemplary embodiment,” “an embodiment,” donot necessarily refer to the same embodiment, although they may.Moreover, any use of phrases like “embodiments” in connection with “theinvention” are never meant to characterize that all embodiments of theinvention must include the particular feature, structure, orcharacteristic, and should instead be understood to mean “at least oneor more embodiments of the invention” includes the stated particularfeature, structure, or characteristic.

The enumerated listing of items does not imply that any or all of theitems are mutually exclusive, unless expressly specified otherwise.

It is understood that the use of a specific component, device and/orparameter names are for example only and not meant to imply anylimitations on the invention. The invention may thus be implemented withdifferent nomenclature and/or terminology utilized to describe themechanisms, units, structures, components, devices, parameters and/orelements herein, without limitation. Each term utilized herein is to begiven its broadest interpretation given the context in which that termis utilized.

Devices or system modules that are in at least general communicationwith each other need not be in continuous communication with each other,unless expressly specified otherwise. In addition, devices or systemmodules that are in at least general communication with each other maycommunicate directly or indirectly through one or more intermediaries.

A description of an embodiment with several components in communicationwith each other does not imply that all such components are required. Onthe contrary a variety of optional components are described toillustrate the wide variety of possible embodiments of the presentinvention.

A “computer” may refer to one or more apparatus and/or one or moresystems that are capable of accepting a structured input, processing thestructured input according to prescribed rules, and producing results ofthe processing as output. Examples of a computer may include: acomputer; a stationary and/or portable computer; a computer having asingle processor, multiple processors, or multi-core processors, whichmay operate in parallel and/or not in parallel; a general purposecomputer; a supercomputer; a mainframe; a super mini-computer; amini-computer; a workstation; a micro-computer; a server; a client; aninteractive television; a web appliance; a telecommunications devicewith internet access; a hybrid combination of a computer and aninteractive television; a portable computer; a tablet personal computer(PC); a personal digital assistant (PDA); a portable telephone; asmartphone, application-specific hardware to emulate a computer and/orsoftware, such as, for example, a digital signal processor (DSP), afield-programmable gate array (FPGA), an application specific integratedcircuit (ASIC), an application specific instruction-set processor(ASIP), a chip, chips, a system on a chip, or a chip set; a dataacquisition device; an optical computer; a quantum computer; abiological computer; and generally, an apparatus that may accept data,process data according to one or more stored software programs, generateresults, and typically include input, output, storage, arithmetic,logic, and control units.

Those of skill in the art will appreciate that where appropriate, one ormore embodiments of the disclosure may be practiced in network computingenvironments with many types of computer system configurations,including personal computers, hand-held devices, multi-processorsystems, microprocessor-based or programmable consumer electronics,network PCs, minicomputers, mainframe computers, and the like. Whereappropriate, embodiments may also be practiced in distributed computingenvironments where tasks are performed by local and remote processingdevices that are linked (either by hardwired links, wireless links, orby a combination thereof) through a communications network. In adistributed computing environment, program modules may be located inboth local and remote memory storage devices.

The example embodiments described herein can be implemented in anoperating environment comprising computer-executable instructions (e.g.,software) installed on a computer, in hardware, or in a combination ofsoftware and hardware. The computer-executable instructions can bewritten in a computer programming language or can be embodied infirmware logic. If written in a programming language conforming to arecognized standard, such instructions can be executed on a variety ofhardware platforms and for interfaces to a variety of operating systems.Although not limited thereto, computer software program code forcarrying out operations for aspects of the present invention can bewritten in any combination of one or more suitable programminglanguages, including an object oriented programming languages and/orconventional procedural programming languages, and/or programminglanguages such as, for example, Hypertext Markup Language (HTML),Dynamic HTML, Extensible Markup Language (XML), Extensible StylesheetLanguage (XSL), Document Style Semantics and Specification Language(DSSSL), Cascading Style Sheets (CSS), Synchronized MultimediaIntegration Language (SMIL), Wireless Markup Language (WML), Java™,Jini™, C, C++, Smalltalk, Perl, UNIX Shell, Visual Basic or Visual BasicScript, Virtual Reality Markup Language (VRML), ColdFusion™ or othercompilers, assemblers, interpreters or other computer languages orplatforms.

Computer program code for carrying out operations for aspects of thepresent invention may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Smalltalk, C++ or the like and conventional proceduralprogramming languages, such as the “C” programming language or similarprogramming languages. The program code may execute entirely on theuser's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer and partly on a remotecomputer or entirely on the remote computer or server. In the latterscenario, the remote computer may be connected to the user's computerthrough any type of network, including a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider).

A network is a collection of links and nodes (e.g., multiple computersand/or other devices connected together) arranged so that informationmay be passed from one part of the network to another over multiplelinks and through various nodes. Examples of networks include theInternet, the public switched telephone network, the global Telexnetwork, computer networks (e.g., an intranet, an extranet, a local-areanetwork, or a wide-area network), wired networks, and wireless networks.

Aspects of the present invention are described above with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems) and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer program instructions. These computer program instructions maybe provided to a processor of a general purpose computer, specialpurpose computer, or other programmable data processing apparatus toproduce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks.

The flowchart and block diagrams in the figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods and computer program products according to variousembodiments. In this regard, each block in the flowchart or blockdiagrams may represent a module, segment, or portion of code, whichcomprises one or more executable instructions for implementing thespecified logical function(s). It should also be noted that, in somealternative implementations, the functions noted in the block may occurout of the order noted in the figures. For example, two blocks shown insuccession may, in fact, be executed substantially concurrently, or theblocks may sometimes be executed in the reverse order, depending uponthe functionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts, or combinations of special purpose hardware andcomputer instructions.

These computer program instructions may also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function/act specified in the flowchart and/or blockdiagram block or blocks.

Further, although process steps, method steps, algorithms or the likemay be described in a sequential order, such processes, methods andalgorithms may be configured to work in alternate orders. In otherwords, any sequence or order of steps that may be described does notnecessarily indicate a requirement that the steps be performed in thatorder. The steps of processes described herein may be performed in anyorder practical. Further, some steps may be performed simultaneously.

It will be readily apparent that the various methods and algorithmsdescribed herein may be implemented by, e.g., appropriately programmedgeneral purpose computers and computing devices. Typically a processor(e.g., a microprocessor) will receive instructions from a memory or likedevice, and execute those instructions, thereby performing a processdefined by those instructions. Further, programs that implement suchmethods and algorithms may be stored and transmitted using a variety ofknown media.

When a single device or article is described herein, it will be readilyapparent that more than one device/article (whether or not theycooperate) may be used in place of a single device/article. Similarly,where more than one device or article is described herein (whether ornot they cooperate), it will be readily apparent that a singledevice/article may be used in place of the more than one device orarticle.

The functionality and/or the features of a device may be alternativelyembodied by one or more other devices which are not explicitly describedas having such functionality/features. Thus, other embodiments of thepresent invention need not include the device itself.

The term “computer-readable medium” as used herein refers to any mediumthat participates in providing data (e.g., instructions) which may beread by a computer, a processor or a like device. Such a medium may takemany forms, including but not limited to, non-volatile media, volatilemedia, and transmission media. Non-volatile media include, for example,optical or magnetic disks and other persistent memory. Volatile mediainclude dynamic random access memory (DRAM), which typically constitutesthe main memory. Transmission media include coaxial cables, copper wireand fiber optics, including the wires that comprise a system bus coupledto the processor. Transmission media may include or convey acousticwaves, light waves and electromagnetic emissions, such as thosegenerated during radio frequency (RF) and infrared (IR) datacommunications. Common forms of computer-readable media include, forexample, a floppy disk, a flexible disk, hard disk, magnetic tape, anyother magnetic medium, a CD-ROM, DVD, any other optical medium, punchcards, paper tape, any other physical medium with patterns of holes, aRAM, a PROM, an EPROM, a FLASH-EEPROM, removable media, flash memory, a“memory stick”, any other memory chip or cartridge, a carrier wave asdescribed hereinafter, or any other medium from which a computer canread.

Where databases are described, it will be understood by one of ordinaryskill in the art that (i) alternative database structures to thosedescribed may be readily employed, (ii) other memory structures besidesdatabases may be readily employed. Any schematic illustrations andaccompanying descriptions of any sample databases presented herein areexemplary arrangements for stored representations of information. Anynumber of other arrangements may be employed besides those suggested bythe tables shown. Similarly, any illustrated entries of the databasesrepresent exemplary information only; those skilled in the art willunderstand that the number and content of the entries can be differentfrom those illustrated herein. Further, despite any depiction of thedatabases as tables, an object-based model could be used to store andmanipulate the data types of the present invention and likewise, objectmethods or behaviors can be used to implement the processes of thepresent invention.

Embodiments of the invention may also be implemented in one or acombination of hardware, firmware, and software. They may be implementedas instructions stored on a machine-readable medium, which may be readand executed by a computing platform to perform the operations describedherein.

More specifically, as will be appreciated by one skilled in the art,aspects of the present invention may be embodied as a system, method orcomputer program product. Accordingly, aspects of the present inventionmay take the form of an entirely hardware embodiment, an entirelysoftware embodiment (including firmware, resident software, micro-code,etc.) or an embodiment combining software and hardware aspects that mayall generally be referred to herein as a “circuit,” “module” or“system.” Furthermore, aspects of the present invention may take theform of a computer program product embodied in one or more computerreadable medium(s) having computer readable program code embodiedthereon.

Unless specifically stated otherwise, and as may be apparent from thefollowing description and claims, it should be appreciated thatthroughout the specification descriptions utilizing terms such as“processing,” “computing,” “calculating,” “determining,” or the like,refer to the action and/or processes of a computer or computing system,or similar electronic computing device, that manipulate and/or transformdata represented as physical, such as electronic, quantities within thecomputing system's registers and/or memories into other data similarlyrepresented as physical quantities within the computing system'smemories, registers or other such information storage, transmission ordisplay devices.

The term “processor” may refer to any device or portion of a device thatprocesses electronic data from registers and/or memory to transform thatelectronic data into other electronic data that may be stored inregisters and/or memory. A “computing platform” may comprise one or moreprocessors.

Those skilled in the art will readily recognize, in light of and inaccordance with the teachings of the present invention, that any of theforegoing steps and/or system modules may be suitably replaced,reordered, removed and additional steps and/or system modules may beinserted depending upon the needs of the particular application, andthat the systems of the foregoing embodiments may be implemented usingany of a wide variety of suitable processes and system modules, and isnot limited to any particular computer hardware, software, middleware,firmware, microcode and the like. For any method steps described in thepresent application that can be carried out on a computing machine, atypical computer system can, when appropriately configured or designed,serve as a computer system in which those aspects of the invention maybe embodied.

It will be further apparent to those skilled in the art that at least aportion of the novel method steps and/or system components of thepresent invention may be practiced and/or located in location(s)possibly outside the jurisdiction of the United States of America (USA),whereby it will be accordingly readily recognized that at least a subsetof the novel method steps and/or system components in the foregoingembodiments must be practiced within the jurisdiction of the USA for thebenefit of an entity therein or to achieve an object of the presentinvention.

All the features disclosed in this specification, including anyaccompanying abstract and drawings, may be replaced by alternativefeatures serving the same, equivalent or similar purpose, unlessexpressly stated otherwise. Thus, unless expressly stated otherwise,each feature disclosed is one example only of a generic series ofequivalent or similar features.

Having fully described at least one embodiment of the present invention,other equivalent or alternative methods of implementing thecertification network 100 according to the present invention will beapparent to those skilled in the art. Various aspects of the inventionhave been described above by way of illustration, and the specificembodiments disclosed are not intended to limit the invention to theparticular forms disclosed. The particular implementation of the loyaltyrewards programs may vary depending upon the particular context orapplication. It is to be further understood that not all of thedisclosed embodiments in the foregoing specification will necessarilysatisfy or achieve each of the objects, advantages, or improvementsdescribed in the foregoing specification.

Claim elements and steps herein may have been numbered and/or letteredsolely as an aid in readability and understanding. Any such numberingand lettering in itself is not intended to and should not be taken toindicate the ordering of elements and/or steps in the claims.

The description of the present invention has been presented for purposesof illustration and description, but is not intended to be exhaustive orlimited to the invention in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of the invention. Theembodiment was chosen and described in order to best explain theprinciples of the invention and the practical application, and to enableothers of ordinary skill in the art to understand the invention forvarious embodiments with various modifications as are suited to theparticular use contemplated.

The Abstract is provided to comply with 37 C.F.R. Section 1.72(b)requiring an abstract that will allow the reader to ascertain the natureand gist of the technical disclosure. It is submitted with theunderstanding that it will not be used to limit or interpret the scopeor meaning of the claims. The following claims are hereby incorporatedinto the detailed description, with each claim standing on its own as aseparate embodiment.

1. A device for structuring and processing data for efficient selectionof a referral provider for a patient, the device comprising: aprocessor, a memory, computer readable instructions that when executedextract a set of utilization logs from a log data structure comprising(i) a set of data each modeling healthcare providers and each associatedby one or more referral logs, and (ii) a group of utilization logs eachassociated with a data from the set of data each modeling the healthcareproviders, wherein the group of utilization logs comprises a firstutilization log of a different patient that was previously served by afirst healthcare provider at a facility to result in a healthcareutilization log, and wherein the first utilization log comprising aprovider UID of the first healthcare provider and a place of servicevalue that describes a type of facility associated with a healthcareutilization record; a referral request agent comprising computerreadable instructions that when executed at least one of generate andreceive a referral profile generated for the patient; a profile matchingengine comprising computer readable instructions that when executedcompares the referral profile to the set of utilization logs; a datasetreduction subroutine comprising computer readable instructions that whenexecuted generate a reduced dataset comprising a subset of utilizationlogs extracted from the set of utilization logs matching the referralprofile, wherein a number of healthcare providers associated with thereduced dataset is compared to a minimum threshold of healthcareproviders to determine a sufficient number of healthcare providerswithin the reduced dataset; a utilization rate routine comprisingcomputer readable instructions that when executed calculates, using thesubset of utilization logs in the reduced dataset, a POS utilizationrate of the first healthcare provider for each instance of the place ofservice value, wherein the POS utilization rate is a set of percentagevalues, each percentage value a number of utilization logs in thereduced dataset comprising an instance of the place of service valuewithin a place of service range relative to a total number ofutilization logs in the reduced dataset; and a set of computer readableinstructions that when executed select the first healthcare provider forinclusion in a referral data based on criteria comprising the POSutilization rate for transmission of at least one of a name of the firsthealthcare provider and the provider UID of the first healthcareprovider to a computing device for generation of a referral selectionfor the patient.
 2. The device of claim 1, further comprising: autilization extraction routine comprising computer readable instructionsthat when executed: determine generation of the healthcare utilizationrecord, wherein the healthcare utilization record comprising a patientUID of the different patient and the provider UID of the firsthealthcare provider; and determine the place of service value thatdescribes the type of facility associated with the healthcareutilization record; a log storage module comprising computer readableinstructions that when executed: generates the first utilization logcomprising the provider UID of the first healthcare provider, the placeof service value, and a utilization time associated with at least one ofproviding a healthcare service to the different patient and generationof the healthcare utilization record; and stores a utilization log inthe log data structure.
 3. The device of claim 2, further comprising: areferral profile generation routine comprising computer readableinstructions that when executed generates the referral profile for thepatient, the referral profile comprising the place of service range anda time range, wherein a referral request is initiated on a userinterface of a clinical documentation workflow of a point-of-careapplication that is run by a second healthcare provider to refer thepatient of the second healthcare provider to the referral provider. 4.The method of claim 1, further comprising: a second set of computerreadable instructions that when executed: apply a utilization ruleset toat least one of score, rank, and qualify the first healthcare providerselected based on criteria comprising the POS utilization rate; and addthe provider UID of the first healthcare provider to the referral data.5. The method of claim 1, further comprising a third set of computerreadable instructions that when executed: transmit the referral dataover a network to the computing device, wherein the computing device isutilized by a second healthcare provider and is running a point-of-careapplication, and wherein the referral data is integrated within a userinterface of a clinical documentation workflow of the point-of-careapplication.
 6. The method of claim 1, further comprising: a patientquery engine comprising computer readable instructions that whenexecuted: query a patient profile of the different patient with apatient UID of the different patient; and extract from the patientprofile of the different patient a patient data comprising at least oneof a demographic data of the different patient, a coverage type of thedifferent patient, and a diagnosis code of the different patient,wherein the first utilization log further comprising the patient data,wherein the referral profile further comprising a patient data range,and wherein the patient data range comprising at least one of ademographic data of the patient, a coverage type of the patient, and adiagnosis code of the patient.
 7. The method of claim 1, wherein theutilization rate routine further comprises computer readableinstructions that when executed: calculates, using a different set ofutilization logs of two or more healthcare providers, a POS utilizationrate of the two or more healthcare providers for each instance of theplace of service value within the place of service range, wherein theselection of the first healthcare provider is based on criteriacomprising the POS utilization rate of the first healthcare providerrelative to a statistical average of the POS utilization rate of the twoor more healthcare providers.
 8. The method of claim 4, furthercomprising: a referral rate routine comprising computer readableinstructions that when executed: calculates, using the subset of the setof referral logs in the reduced dataset, an inbound re-referral rate ofthe referral healthcare provider; wherein the inbound re-referral rateis calculated as a proportion of (i) the subset of the set of referrallogs that each store a database association drawn into the data modelingthe referral healthcare provider and that comprise a databaseassociation linked to one or more of the subset of the set of referrallogs that store a database association drawn out of the data modelingthe referral healthcare provider, where a timestamp of each referral logstoring the database association drawn into the data modeling thereferral healthcare provider and a timestamp of each referral logstoring the database association drawn out of the data modeling thereferral healthcare provider are within a first time period value,relative to (ii) other instances within the subset of the set ofreferral logs that each store database associations drawn into the datamodeling the referral healthcare provider, wherein the place of servicevalue is stored in computer memory as a POS code value, and wherein atleast one of the place of service range, a patient data range, and atime range of the referral profile is selected by the clinician throughthe point-of-care application.
 9. A method for structuring andprocessing data for efficient selection of a referral provider for apatient, the method comprising: extracting a set of utilization logsfrom a log data structure comprising (i) a set of data each modelinghealthcare providers and each associated by one or more referral logs,and (ii) a group of utilization logs each associated with a data fromthe set of data each modeling the healthcare providers, wherein thegroup of utilization logs comprises a first utilization log of adifferent patient that was previously served by a first healthcareprovider at a facility to result in a healthcare utilization log, andwherein the first utilization log comprising a provider UID of the firsthealthcare provider and a place of service value that describes a typeof facility associated with a healthcare utilization record; receiving areferral profile generated for the patient; comparing the referralprofile to the set of utilization logs; generating a reduced datasetcomprising a subset of utilization logs extracted from the set ofutilization logs matching the referral profile, wherein a number ofhealthcare providers associated with the reduced dataset is compared toa minimum threshold of healthcare providers to determine a sufficientnumber of healthcare providers within the reduced dataset; calculating,using the subset of utilization logs in the reduced dataset, a POSutilization rate of the first healthcare provider for each instance ofthe place of service value, wherein the POS utilization rate is a set ofpercentage values, each percentage value a number of utilization logs inthe reduced dataset comprising an instance of the place of service valuewithin a place of service range relative to a total number ofutilization logs in the reduced dataset; and selecting the firsthealthcare provider for inclusion in a referral data based on criteriacomprising the POS utilization rate for transmission of at least one ofa name of the first healthcare provider and the provider UID of thefirst healthcare provider to a computing device for generation of areferral selection for the patient.
 10. The method of claim 9, furthercomprising: determining generation of the healthcare utilization record,wherein the healthcare utilization record comprising a patient UID ofthe different patient and the provider UID of the first healthcareprovider; determining the place of service value that describes the typeof facility associated with the healthcare utilization record;generating the first utilization log comprising the provider UID of thefirst healthcare provider, the place of service value, and a utilizationtime associated with at least one of providing a healthcare service tothe different patient and generation of the healthcare utilizationrecord; and storing a utilization log in the log data structure.
 11. Themethod of claim 10, further comprising: generating the referral profilefor the patient, the referral profile comprising the place of servicerange and a time range, wherein a referral request is initiated on auser interface of a clinical documentation workflow of a point-of-careapplication that is run by a second healthcare provider to refer thepatient of the second healthcare provider to the referral provider. 12.The method of claim 9, further comprising: applying a utilizationruleset to at least one of score, rank, and qualify the first healthcareprovider selected based on criteria comprising the POS utilization rate;and adding the provider UID of the first healthcare provider to thereferral data.
 13. The method of claim 9, further comprising:transmitting the referral data over a network from a server to thecomputing device, wherein the computing device is utilized by a secondhealthcare provider and is running a point-of-care application, andwherein the referral data is integrated within a user interface of aclinical documentation workflow of the point-of-care application. 14.The method of claim 9, further comprising: querying a patient profile ofthe different patient with a patient UID of the different patient; andextracting from the patient profile of the different patient a patientdata comprising at least one of a demographic data of the differentpatient, a coverage type of the different patient, and a diagnosis codeof the different patient, wherein the first utilization log furthercomprising the patient data, wherein the referral profile furthercomprising a patient data range, and wherein the patient data rangecomprising at least one of a demographic data of the patient, a coveragetype of the patient, and a diagnosis code of the patient.
 15. The methodof claim 9, further comprising: calculating, using a different set ofutilization logs of two or more healthcare providers, a POS utilizationrate of the two or more healthcare providers for each instance of theplace of service value within the place of service range, wherein theselection of the first healthcare provider is based on criteriacomprising the POS utilization rate of the first healthcare providerrelative to a statistical average of the POS utilization rate of the twoor more healthcare providers.
 16. The method of claim 12, furthercomprising: receiving a selection of a second healthcare provider from aclinician through a point-of-care application; automatically schedulingan appointment for the patient with the first healthcare provider;extracting from a patient profile of the patient a location dataassociated with the patient; determining the second healthcare provideris within a predetermined distance based on the location data;determining a type of service associated with the healthcare utilizationrecord, wherein the type of service is specified by a type of servicevalue is at least one of a service category and a procedure code value,wherein the procedure code value is a CPT code value, wherein theutilization ruleset determines that the instance of the place of servicevalue is a preferred instance of the place of service value based oncriteria comprising the type of service, wherein the place of servicevalue is stored in computer memory as a POS code value, and wherein atleast one of the place of service range, a patient data range, and atime range of the referral profile is selected by the clinician throughthe point-of-care application.
 17. A computer readable media comprisingcomputer executable instructions that when executed: extract a set ofutilization logs from a log data structure comprising (i) a set of dataeach modeling healthcare providers and each associated by one or morereferral logs, and (ii) a group of utilization logs each associated witha data from the set of data each modeling the healthcare providers,wherein the group of utilization logs comprises a first utilization logof a different patient that was previously served by a first healthcareprovider at a facility to result in a healthcare utilization log, andwherein the first utilization log comprising a provider UID of the firsthealthcare provider and a place of service value that describes a typeof facility associated with a healthcare utilization record; receive areferral profile generated for the patient; compare the referral profileto the set of utilization logs; generate a reduced dataset comprising asubset of utilization logs extracted from the set of utilization logsmatching the referral profile, wherein a number of healthcare providersassociated with the reduced dataset is compared to a minimum thresholdof healthcare providers to determine a sufficient number of healthcareproviders within the reduced dataset; calculate, using the subset ofutilization logs in the reduced dataset, a POS utilization rate of thefirst healthcare provider for each instance of the place of servicevalue, wherein the POS utilization rate is a set of percentage values,each percentage value a number of utilization logs in the reduceddataset comprising an instance of the place of service value within aplace of service range relative to a total number of utilization logs inthe reduced dataset; and select the first healthcare provider forinclusion in a referral data based on criteria comprising the POSutilization rate for transmission of at least one of a name of the firsthealthcare provider and the provider UID of the first healthcareprovider to a computing device for generation of a referral selectionfor the patient.
 18. The computer readable media of claim 17, furthercomprising computer executable instructions that when executed:determine generation of the healthcare utilization record, wherein thehealthcare utilization record comprising a patient UID of the differentpatient and the provider UID of the first healthcare provider; determinethe place of service value that describes the type of facilityassociated with the healthcare utilization record; generate the firstutilization log comprising the provider UID of the first healthcareprovider, the place of service value, and a utilization time associatedwith at least one of providing a healthcare service to the differentpatient and generation of the healthcare utilization record; store autilization log in the log data structure; generate the referral profilefor the patient, the referral profile comprising the place of servicerange and a time range, wherein a referral request is initiated on auser interface of a clinical documentation workflow of a point-of-careapplication that is run by a second healthcare provider to refer thepatient of the second healthcare provider to the referral provider. 19.The computer readable media of claim 17, further comprising computerexecutable instructions that when executed: query a patient profile ofthe different patient with a patient UID of the different patient; andextract from the patient profile of the different patient a patient datacomprising at least one of a demographic data of the different patient,a coverage type of the different patient, and a diagnosis code of thedifferent patient, wherein the first utilization log further comprisingthe patient data, wherein the referral profile further comprising apatient data range, and wherein the patient data range comprising atleast one of a demographic data of the patient, a coverage type of thepatient, and a diagnosis code of the patient; transmit the referral dataover a network from a server to the computing device, wherein thecomputing device is utilized by a second healthcare provider and isrunning a point-of-care application, and wherein the referral data isintegrated within a user interface of a clinical documentation workflowof the point-of-care application. calculate, using a different set ofutilization logs of two or more healthcare providers, a POS utilizationrate of the two or more healthcare providers for each instance of theplace of service value within the place of service range, wherein theselection of the first healthcare provider is based on criteriacomprising the POS utilization rate of the first healthcare providerrelative to a statistical average of the POS utilization rate of the twoor more healthcare providers.
 20. The computer readable media of claim17, further comprising computer executable instructions that whenexecuted: apply a utilization ruleset to at least one of score, rank,and qualify the first healthcare provider selected based on criteriacomprising the POS utilization rate; and add the provider UID of thefirst healthcare provider to the referral data. receive a selection of asecond healthcare provider from a clinician through a point-of-careapplication; automatically scheduling an appointment for the patientwith the first healthcare provider; extract from a patient profile ofthe patient a location data associated with the patient; determine thesecond healthcare provider is within a predetermined distance based onthe location data; determine a type of service associated with thehealthcare utilization record, wherein the type of service is specifiedby a type of service value is at least one of a service category and aprocedure code value, wherein the procedure code value is a CPT codevalue, wherein the utilization ruleset determines that the instance ofthe place of service value is a preferred instance of the place ofservice value based on criteria comprising the type of service, whereinthe place of service value is stored in computer memory as a POS codevalue, and wherein at least one of the place of service range, a patientdata range, and a time range of the referral profile is selected by theclinician through the point-of-care application.